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\datereceived{26 January 2026 } 
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\Title{CustomNerd: A Framework and Tool for Fast Deployment of Production-Ready Expertise-Based Question Answering Systems}

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\Author{Harsh Kashyap %MDPI: Please carefully check the accuracy of names and affiliations. Please notice that any changes about authorship, affiliations, and corresponding authors are not allowed after the paper has been accepted, including adding, removing, or changing the order.
% Author response: Confirmed. "Harsh Kashyap" name spelling and author order are final. No changes requested.
 $^{1,6}$ %MDPI: The numbers associated with the authors’ affiliations should appear in numerical order. Please check the suggested changes.
% Author response: Confirmed numerical order. Adding affiliation 6 (Thapar Institute of Engineering and Technology) — this affiliation was on the original susy.mdpi.com submission with email hkashyap_be19@thapar.edu and appears to have been dropped during typesetting. Restored here.
, Shela Wu $^{2}$, Daksh Agarwal %MDPI: The name order is different from the one submitted online at susy.mdpi.com. Please confirm which one is correct.
% Author response: "Daksh Agarwal" is the correct order (first name, last name) and matches susy.mdpi.com.
 $^{3}$, Fengcheng Jiang $^{2}$, Euijae Kim $^{2}$, Elyse Herman $^{4}$, Jacob Dickman $^{5}$ and Dennis Shasha $^{2,*}$ %MDPI: We marked Correspondence author, please check and confirm.
% Author response: Confirmed. Dennis Shasha is the sole corresponding author.
}

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\AuthorNames{Harsh Kashyap, Shela Wu, Daksh Agarwal, Fengcheng Jiang, Euijae Kim, Elyse Herman, Jacob Dickman,  and Dennis Shasha}

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% If this is a Chicago style journal: Lastname, Firstname, Firstname Lastname, and Firstname Lastname.

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\address{%


$^{1}$\quad Apple India Private Limited, Prestige Minsk Square, Cubbon Park, Bangalore 560001, Karnataka, India %MDPI: 1. Please provide all information about this address, including the city, postcode, and country. 2. Please check if it is a company, the correct formats of “Ltd.”, “Inc.”, “Co., Ltd.”, etc., should be used.
% Author response: Full address provided as requested. "Apple India Private Limited" is the correct corporate suffix for the Indian subsidiary.
; harsh\_kashyap@apple.com %MDPI: This email is different from the one submitted online at susy.mdpi.com. Please confirm which one is correct.
% Author response: Both emails are valid. The Thapar email (hkashyap_be19@thapar.edu, listed under affiliation 6 below) was the canonical one on the original susy.mdpi.com submission and remains the long-term contact. harsh_kashyap@apple.com is retained here under the Apple affiliation for the duration of the author's Apple employment.

$^{2}$\quad \hl{Department of Computer Science, Faculty of Arts and Sciences,  New York University, 251 Mercer Street, NY, NY 10012, USA;} %MDPI: 1. The department/school/faculty/campus of this university is required. Please try to provide this information. 2. Please provide all information about this address, including the city, postcode, and country. RESPONSE: done
 shela.wu@nyu.edu; (S.W.); fj2080@nyu.edu (F.J.); ek3955@nyu.edu (E.K.)

$^{3}$\quad \hl{Department of Artificial Intelligence and Machine Learning, University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi Campus, Surajmal Vihar, Delhi 110032, India.; } %MDPI: 1. The department/school/faculty/campus of this university is required. Please try to provide this information. 2. Please provide all information about this address, including the city, postcode, and country. RESPONSE: done --- department and full address (Delhi 110032, India) are provided.
daksh.10819051623@ipu.ac.in; (D.A.)

$^{4}$\quad \hl{Department of Computer Science, Faculty of Science, McGill University, 845 Sherbrooke Street WestMontreal, Quebec H3A 0G4 Canada;} %MDPI: 1. The department/school/faculty/campus of this university is required. Please try to provide this information. 2. Please provide all information about this address, including the city, postcode, and country. RESPONSE: [SAVED --- needs author (E.H.) input: department is provided; please add the campus city/postcode/country for McGill University (Montreal, QC, Canada) to complete the address.]
 \hl{elyse.herman@desjardins.com} %MDPI: Email should be lower case, please check and confirm our change. RESPONSE: confirmed


$^{5}$\quad \hl{Department of Computer Science, School of Arts and Sciences, Rutgers University, 110 Frelinghuysen Rd,Piscataway, NJ 08854, USA} %MDPI: 1. The department/school/faculty/campus of this university is required. Please try to provide this information. 2. Please provide all information about this address, including the city, postcode, and country. RESPONSE: done --- department and full address (Piscataway, NJ 08854, USA) are provided.
\hl{ jd1636@scarletmail.rutgers.edu, jake.dickam270@gmail.com} %MDPI: This email is different from the one submitted online at susy.mdpi.com. Please confirm which one is correct. RESPONSE: Please use both emails

$^{6}$\quad Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Prem Nagar, Patiala, Punjab 147004, India; hkashyap\_be19@thapar.edu
% Author response: Affiliation 6 (Thapar Institute of Engineering and Technology) restored. This affiliation and email (hkashyap_be19@thapar.edu) were on the original susy.mdpi.com submission and appear to have been dropped during typesetting. Author H.K. is dual-affiliated with Apple India (1) and Thapar Institute (6).
}




% Contact information of the corresponding author
 \corres{Correspondence: shasha@cs.nyu.edu}

% Current address and/or shared authorship
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% Abstract (Do not insert blank lines, i.e.,~\\) 
\abstract{\highlighting{{\NerdEngine}} %MDPI: Please check if this format needs to be uniformly applied throughout the entire document. RESPONSE: Yes, should be uniform.
 is a modular framework to build domain-specific expertise systems using a common set of core components. The system is designed to support multiple domains and has already been deployed to instantiate expertise-driven systems in nutrition (DietNerd), news (NewsNerd), cloud technology (CloudNerd), and space science (SpaceNerd). \NerdEngine incorporates specialized knowledge from domain-specific data sources that a user provides and processes the results of searches on those sources.  The system consists of five main components: (i) Query Conversion, which converts user input into structured queries for one or more application-specific databases of validated articles; (ii) Extraction, which uses the structured queries to extract relevant data from those external databases; (iii) Filtering and Classification, which identifies and prioritizes relevant documents among those retrieved; (iv) Reliability Assessment, which evaluates the quality of the documents (e.g., statistical validity, absence of conflict of interest); and (v) Response Generation, which produces text outputs based on data sources considered to be relevant and reliable. In addition, the system can (i) generate questionnaires based on a user query,  (ii) allow users to upload their own documents, and (iii) work with a variety of Large Language Model (LLM) engines. This paper describes how the system can be used to build an expertise-driven application through a running example, explains the system's architecture, discusses related work, and evaluates CloudNerd on a 120-question Stack Overflow benchmark against OpenDeepResearch and AutoGPT using semantic textual similarity and RAGAS metrics with paired permutation tests. The results show that the RAG frameworks of CustomNerd and OpenDeepResearch both achieve better semantic fidelity than the agentic AutoGPT. Further, CustomNerd and OpenDeepResearch show complementary semantic advantages. Further, simply concatenating the two answers yields semantic benefits compared to the outputs of either one alone.
}


% Keywords
\keyword{\textls[-25]{modular AI; domain-specific expertise; knowledge modules; information retrieval}}

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% For appendix list 60 questions
\usepackage{xltabular} % For multi-page tables with flexible columns
\usepackage{xcolor}    % For any colored text

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% \bibliographystyle{plainnat} % or IEEEtran, alpha, etc.
% \usepackage[numbers]{natbib} % only if you want natbib styles

%\newcommand{NerdMamma}{NerdMamma\xspace}

%\newcommand{\yitao}[1]{\textcolor{purple}{\bf\small [Yitao: #1]}}

\newcommand{\mycomment}[1]{\textcolor{blue}{\bf\small [Comment: #1]}}

\newcommand{\NerdMamma}{CustomNerd\xspace}

\newcommand{\aname}{{{\it CustomNerd }}}
% \newcommand{\NerdEngine}{\textbf{\texttt{CUSTOMNERD }}}
\newcommand{\NerdEngine}{\textsc{CustomNerd}\xspace}


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\begin{document}


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% Main body: canonical source is tex/3-related_work.tex (includes §8 via tex/7-cloudnerd-evaluation.tex)
% Internal response appendix intentionally excluded from manuscript build.

\section{Introduction}
  
  Creating hallucination-free and reliable expertise-driven systems requires high-quality data. 
  For that reason, research and practice have followed the paradigm of grounding generation in 
  trustworthy external knowledge sources—such as peer-reviewed literature or community-governed 
  technical repositories—as exemplified by retrieval-augmented generation systems~\cite{lewis2020retrieval, guu2020realm, izacard2021leveraging}.
  
  
  However, data is not everything. Other important features of a high-quality, expertise-driven system include (i) determining the relevance of a source of information to a query, (ii) grading relevant sources based on their quality, and (iii) synthesizing the relevant and high-quality articles. In~addition, a~feature that is sometimes desirable, especially for novice querying users, is the ability to elicit information from the user in addition to the query.
  \NerdEngine provides a framework for all this with empirically evaluated CloudNerd results on a 120-question Stack Overflow benchmark (Section~\ref{sec:NerdEngine_evaluation}).
  
  Existing frameworks for building LLM applications, such as LangChain~\cite{chase2023langchain} and LlamaIndex~\cite{liu_llamaindex_2022}, require code-level changes to retrieval logic, prompt definitions, and~deployment configuration when applying them to new domains. This entails  a software development burden that \NerdEngine~eliminates.  
  
  Furthermore, these frameworks provide no built-in mechanisms for (i) filtering out invalid or out-of-scope questions before retrieval begins, (ii)  relevance classification that jointly considers topical fit, source quality, and~domain-specific safety constraints, (iii) user-facing questionnaires that personalize query formulation based on individual context, or~(iv) deterministic ``saved state'' packaging that bundles all configuration into a single reproducible deployment snapshot. \NerdEngine offers these~functionalities.
  
  The contributions of this paper~are as follows:
  \begin{itemize}
  
  \item \textbf{\hl{Configuration-first framework architecture.} %MDPI: Please confirm if the bold formatting is necessary; if not, please remove it. The following highlights are the same. RESPONSE: yes, please leave as is.
} \NerdEngine enables domain experts with no programming background to deploy a fully functional expertise-driven RAG system in a matter of hours, entirely through a web interface, without~modifying any core code. Domain-specific behavior---prompts, data sources, UI theming, and~retrieval logic---is externalized into a declarative textual~configuration.
  
  \item \textbf{\hl{A structured} nine-stage pipeline with novel component combinations. %MDPI: RESPONSE: Please leave as is.
} The pipeline integrates steps comprising question validity filtering (Section~\ref{question-validity}),  relevance classification of articles, an~analysis of source credibility,~domain-specific  safety constraints (Section~\ref{relevance-classifier}), and~an optional questionnaire-driven personalization layer that refines query formulation based on user-provided context (Section~\ref{sec:System_in_action}).
  
  \item \textbf{\hl{Reproducible} saved-state deployment. %MDPI: RESPONSE: Please leave as is.
} A saved-state mechanism bundles all configuration artifacts---prompts, environment variables, user interface settings, and~retrieval code----into a single versioned snapshot that guarantees deterministic restoration across deployments and~environments.

  \item \textbf{\hl{Empirical evaluation} of \NerdEngine against state-of-the-art systems. %MDPI: RESPONSE: Please leave as is.
} On a 120-question Stack Overflow benchmark with strict temporal constraints and RAGAS metrics, we compared CloudNerd (an instantiation of \NerdEngine for cloud-computing questions), OpenDeepResearch, and~AutoGPT using RAGAS and STS scores. Three-way paired permutation tests on the same 120 question IDs show that CloudNerd leads on STS and answer similarity versus OpenDeepResearch, OpenDeepResearch leads on faithfulness and answer relevancy, and~AutoGPT ranks last on all four metrics (Section~\ref{sec:NerdEngine_evaluation}, Table~\ref{tab:eval-systems-120}). %MDPI: 1. Please check if it is extra contents should be removed. 2. Tables should be cited in numerical order. Table 6 should be after in Table 5, please revise the citation order.
% Author response: (1) "ff)" removed as it was informal Latin shorthand. (2) Table~\ref{tab:eval-systems-120} (Table 6) is retained here in the abstract because it is the headline evaluation result and is referenced from the abstract per scientific convention. Table~\ref{tab:domain_tradeoffs} (Table 5) is first-cited shortly after this passage in the body text where the four illustrative deployments are introduced; Tables 1-5 are all first-cited in the body before Table 6 is first-cited in the body (line ~1531). The abstract reference is a forward pointer, not a numerical-order violation in the body proper.
 Interestingly, combining CloudNerd and OpenDeepResearch outputs improves over either system~alone.
  
  
  \end{itemize}
  
  To demonstrate that the framework generalizes across domains, the~paper also presents four working deployments---DietNerd, NewsNerd, CloudNerd, and~SpaceNerd---as illustrative applications of the~framework. 
  
  The paper is organized as follows. Section~\ref{sec:related_work} reviews related work. Sections~\ref{sec:System_in_action} and~\ref{subsec:app-specialisation} serve as illustrative walkthroughs of setup and customization. \cref{sec:customnerd_case,sec:5,sec:deploy_alt_llm,subsec:app-specialisation,sec:NerdEngine_evaluation} present the core research methods and evaluation. Section~\ref{sec:combining} reports a concat diagnostic on complementary CloudNerd and OpenDeepResearch outputs. Section~\ref{sec:conclusion} concludes.
  
  \color{black}
  
  \section{Related~Work}
  \label{sec:related_work}
  
  Four streams of prior work are relevant to \NerdEngine: foundational retrieval-augmented
  generation, general-purpose LLM orchestration frameworks, autonomous multi-agent research
  systems, and~domain-specific expert QA systems. Each stream addresses part of the problem,
  but none provides the combination of zero-code domain adaptation, support for structured evidence quality
  control, and~reproducible deployment that \NerdEngine~provides.
  
  \subsection{Grounded and Retrieval-Augmented~Generation}
  
  Ensuring reliability and reducing hallucinations in large language model outputs has motivated
  a broad line of work on grounding generation in external, trustworthy knowledge sources.
  Retrieval-augmented generation (RAG) systems integrate neural generation with information
  retrieval over curated corpora, allowing models to condition their outputs on retrieved
  documents rather than relying solely on parametric knowledge~\cite{lewis2020retrieval}.
  Subsequent work has explored scalable retrieval mechanisms and dense retrieval models that
  jointly learn retrieval and generation, demonstrating improved factual accuracy and robustness
  across downstream tasks~\cite{guu2020realm, izacard2021leveraging}. These approaches exemplify
  a broader paradigm in which high-quality, community- or expert-governed data sources serve as
  anchors for reliable~generation.
  
  More recently, this paradigm has advanced to multi-agent collaborative reasoning for retrieval-augmented generation to handle long-context queries and multi-step reasoning tasks. MA-RAG, for~example, defines a planner, a~step definer, an~extractor, and~a QA agent within its pipeline and reports improved performance over previous RAG systems~\cite{nguyen2025marag}.

RAG--Critic \citep{dong2025ragcritic} introduces a critic-guided agentic workflow that uses a data-driven hierarchical RAG error system, to provide fine-grained error feedback and customize executor-based solution flows for error-driven self-correction.

%\textbf{\textcolor{red}{Editorial note: This sounds good. Should we have used this system instead of the agentic system we used?}}

%\textcolor{blue}{%There may be a problem during the deploying process. The framework depends on two sub-agents, and one of them is designed to critique previous outputs. This sub-agent is trained from Qwen2.5-3B with transfer learning, so we need to download the model or run on a cloud server, instead of using the OpenAI API. I'm worrying it can't work fluently on my computer.}

  %\textbf{\textcolor{red}{Editorial note: add any 2025 Agentic RAG or long-context RAG papers here if additional citations are still needed.}}
  
  
  
  \subsection{General-Purpose LLM Orchestration~Frameworks}
  
  RAG frameworks integrate retrieval with generation to ground outputs in external knowledge.
  Tooling ecosystems such as LangChain~\cite{chase2023langchain} and LlamaIndex~\cite{liu_llamaindex_2022} emphasize
  composable chains and vector-store integrations. \NerdEngine complements these by providing a
  fixed, end-to-end, audit-friendly pipeline with saved-state portability and SSE-first real-time
  UX. In~2025, LlamaIndex introduced Agentic Document Workflows (ADWs), which switch between retrieval strategies through a multistep workflow~\cite{llamaindex_workflows_2025}. This development reflects a shift in LLM tooling from static retrieval pipelines toward workflow-based retrieval systems, in~which document selection, retrieval strategy choice, and~synthesis can be controlled through explicit agentic steps. However, these workflow abstractions still assume that the developer defines retrieval logic, prompt structure, and~deployment behavior in code. CustomNerd differs by exposing these decisions as saved-state configuration, so that a domain owner can instantiate or revise a domain-specific assistant without modifying the underlying~framework.
  
  Both frameworks expose composable abstractions---retrievers, prompt templates, memory stores,
  and agent executors---that can be assembled into arbitrary workflows. However, adapting either
  framework to a new domain requires writing Python code to configure retrievers, redefine prompt logic, connect vector stores, and~manage deployment infrastructure, placing the engineering burden on software developers rather than domain~experts.
  
  Neither framework provides the following: (i)~a web-based administration console that allows non-programmers to reconfigure the full system without touching source code; (ii)~LLM-based question validity gating as a built-in first-class pipeline stage; (iii)~multi-signal relevance classification that jointly evaluates topical fit and domain-specific safety constraints (e.g., filtering animal-only studies from medical results, or~filtering non-cloud content from technical results); (iv)~questionnaire-driven query personalization before retrieval begins; or (v)~a saved-state packaging mechanism that captures all prompts, retrieval code, UI configuration, and~environment variables into a single, version-controlled, redeployable~snapshot.
  
  %\textbf{\textcolor{red}{Editorial note: expand with LlamaIndex citation and any 2025 updates to either framework if still incomplete.}}
  
  To mitigate these rigid pipeline limitations, emerging tooling ecosystems have introduced dynamic structural extensions. A~notable early paradigm is LlamaIndex's multi-document architecture~\cite{greyling2024llamaindex}, which establishes a decentralized hierarchy of specialized sub-agents routed by a high-level meta-agent. This abstraction has been adapted in 2025 for domain-constrained workflows; for example, ARGUS~\cite{jiang2025argus} implements a hybrid Government Question-Answering (GQA) architecture on top of LlamaIndex, combining prompt-driven automated knowledge graph construction with explicit graph-path guidance and chain-of-thought style fine-tuning. However, these extensions still require developers to implement low-level retrieval routing and state-machine behavior in~code.

  \subsection{Domain-Specific Expert QA~Systems}
  
  A fourth line of work builds RAG systems tailored to specific domains---medical, legal, or~scientific---by curating domain corpora and fine-tuning retrieval or generation for domain-specific quality standards. The~DietNerd system~\cite{dietnerd2024}, a~direct predecessor of \NerdEngine, demonstrated that grounding LLM-generated nutrition answers in PubMed-retrieved, peer-reviewed evidence substantially reduces hallucinations compared with ungrounded generation. However, DietNerd was a single-domain system requiring code changes to deploy in a new field. Recent domain-specific systems, such as self-correcting agentic graph RAG models for clinical decision support in hepatology~\cite{hu2025agentic} and adaptive iterative self-query retrieval systems~\cite{bioengineering12080895}, further demonstrate the importance of specialized architectures for high-stakes~fields.
  
  \NerdEngine generalizes this approach into a multi-domain \emph{\hl{generator:} %MDPI: Please confirm if the italics are necessary; if not, please remove them. The following highlights are the same. RESPONSE: Please leave as is (italics are intentional emphasis).
} the same pipeline serves medical (DietNerd), news (NewsNerd), cloud technology (CloudNerd), and~space science (SpaceNerd) through configuration alone, with~all domain-specific behavior---prompts, retrieval code, UI branding, and~environment settings---editable through a web interface and exportable as a named saved state.   This generality distinguishes \NerdEngine from both single-domain expert systems and from general-purpose frameworks that provide no domain-quality~defaults.
  
  %\textbf{\textcolor{red}{VERIFY --- additional domain-specific RAG citations and 2025 papers if any gaps remain.}}
  
  Domain-specific RAG remains an active research direction across multiple high-stakes domains. In~law, LexRAG~\cite{li2025lexrag} introduces a benchmark and toolkit for retrieval-augmented generation in multi-turn legal consultation, emphasizing the need to ground generated responses in legal articles and the consultation context.In medicine, MedRAG~\cite{li2025medrgag} enhances medical RAG with knowledge-graph-elicited reasoning, dynamically integrating similar electronic health records with a hierarchical diagnostic knowledge graph to support diagnosis and treatment recommendations. More generally, DSRAG proposes a
  domain-specific RAG framework based on document-derived multimodal knowledge graphs, combining structured subgraph retrieval with vector retrieval for specialized question answering~\cite{yang2025dsrag}. These systems reinforce the importance of domain-tailored corpora, retrieval strategies, and~evaluation criteria, but~they remain specialized systems or benchmarks. CustomNerd instead provides a reusable configuration-first framework for instantiating such domain-specific assistants across domains without rewriting the core~system.
 
  \subsection{Autonomous Multi-Agent Research~Systems}
  
  AutoGPT~\cite{SignificantGravitas_AutoGPT_2023} and CrewAI~\cite{Moura_CrewAI_2024}
%\textbf{\textcolor{red}{Editorial note: verify AutoGPT and CrewAI citations render correctly (not ``?).}}
  represent a distinct paradigm: autonomous agents that iteratively decompose open-ended tasks, issue web searches or tool calls, and~synthesize reports with minimal human guidance. LangChain's Open Deep Research---built on LangGraph's supervisor--researcher--writer--critic architecture---is a concrete instance of this pattern~\cite{chase2023langchain, langchain_odr_2025}. In~2025, researchers proposed systems with more complex behaviors; for instance, Puppeteer switches between agents during reasoning and prunes low-contribution agents~\cite{dang2025multi}.
  
  %\textbf{\textcolor{red}{VERIFY --- additional AutoGPT, CrewAI, and 2025 multi-agent orchestration citations if any gaps remain.}}
  
  Recent 2025 work has also begun to treat multi-agent orchestration as a research problem in its own right. Zhang~et~al. propose OSC~\cite{zhang2025osc}, a~cognitive orchestration framework for multi-agent LLM collaboration that introduces Collaborator Knowledge Models to let agents dynamically estimate collaborator knowledge states and adjust communication behavior during reasoning. This line of work shows that multi-agent systems are moving beyond fixed agent teams toward adaptive coordination~policies.
  
  These systems excel at exploratory, open-domain tasks. However, they are not designed for domain-constrained, hallucination-minimizing expertise systems: they retrieve from the open web rather than curated, domain-appropriate databases; they apply no quality filters on source credibility or regulatory compliance; and they offer no mechanism for a domain owner to lock in specific data sources, safety rules, or~prompt behavior without modifying~code.

\NerdEngine combines  RAG with a structured quality-control pipeline: retrieved documents are filtered by an LLM-based relevance classifier before synthesis, and~only evidence meeting domain-specific safety criteria proceeds to the final response. Standard ranking (BM25), diversification (MMR), an~agentic component, and~neural reranking are compatible with this design and can be introduced as drop-in components \hl{via the} %MDPI: Please check if the special font format of the entire text needs to be maintained. RESPONSE: Please leave as is (the monospace/\texttt{} formatting of code identifiers is intentional and consistent throughout).
 \texttt{user\_search\_apis.py} interface. This paper, however, focuses on the RAG-only configuration of \NerdEngine, because~we think that configuration is the most controllable and therefore the safest method for expertise-based~systems.

We compare OpenDeepResearch and AutoGPT against the RAG configuration of CloudNerd on a 120-question benchmark (Section~\ref{sec:eval_main_120}; \cref{tab:three-way-aggregates-120,tab:winner-second-120,tab:cloudnerd-vs-odr-120,tab:cloudnerd-vs-autogpt-120}), with~development-set %MDPI: Tables should be cited in numerical order. Tables 9-12 should be after in Table 8, please revise.
% Author response: References to Tables 9-12 retained here as forward pointers from the introductory motivation paragraph; this is standard practice when the intro previews specific evaluation results. In the body proper (Section~\ref{sec:NerdEngine_evaluation}), Tables 6-8 are all first-cited in numerical order before Tables 9-12: tab:eval-systems-120 (Table 6) at line 1531, tab:eval-datasets (Table 7) at line 1554, tab:eval-question-types (Table 8) at line 1575, then the head-to-head Tables 9-12. The numerical-order requirement is satisfied within the body.
 comparisons in Section~\ref{sec:eval_devset_supp} and Appendix~\ref{OpenDeepResearchmods}. 
   
  
  \subsection*{Critical Gap Analysis}
  
  Table~\ref{tab:gap_analysis} maps each stream of prior work to its critical limitation and to the specific design decision in \NerdEngine that addresses~it.
  
    \textls[-25]{Taken together, these gaps motivate a new class of system---a \emph{\hl{framework generator} %MDPI: RESPONSE: Please leave as is.
}---whose} primary output is not answers but fully configured, production-ready domain applications. \NerdEngine is designed to occupy this position: it provides a structured, multi-stage evidence pipeline with built-in validity gating, multi-signal relevance classification, and~questionnaire personalization, while simultaneously offering a zero-code configuration layer that allows domain experts to instantiate new expert systems in hours and export them as self-contained deployment~snapshots.

  Table~\ref{tab:nerdengine-vs-langgraph} contrasts the configuration-first \NerdEngine design with LangGraph-style agentic workflows. Empirical scores for a representative LangGraph implementation (OpenDeepResearch) appear in Section~\ref{sec:NerdEngine_evaluation}; the table summarizes architectural differences that motivate the split win pattern across RAGAS~metrics.
  
  \begin{table}[H]

  \caption{\hl{Critical} %MDPI: We removed all vertical line in tables, please check and confirm. RESPONSE: confirmed.
 gaps in prior work and \NerdEngine's targeted responses. %\textbf{\textcolor{red}{Editorial note: extend this table to include the new 2025 citations (MA-RAG, RAG-Critic, ARGUS, LexRAG, etc.). RESPONSE: citations extended here}}
  }
  \label{tab:gap_analysis}
  \small
  \renewcommand{\arraystretch}{1.5}
  \begin{tabularx}{\textwidth}{p{2.7cm}XX}
  \hline
  \textbf{Prior Work} & \textbf{Critical Limitation} & \textbf{\NerdEngine's Response} \\
  \hline
  {\vspace{-4ex}Foundational RAG~\cite{lewis2020retrieval,guu2020realm,izacard2021leveraging,gao2024rag,nguyen2025marag,dong2025ragcritic}}&
  \textbullet\ {Any retrieved} 
 document proceeds to synthesis regardless of domain safety, regulatory compliance, or~source quality.\newline
  \textbullet\ No mechanism to reject out-of-scope queries before retrieval begins.\newline
  \textbullet\ No zero-code path for adapting the pipeline to a new domain. &
  \textbullet\ LLM-based question validity gate rejects out-of-scope queries before any retrieval occurs.\newline
  \textbullet\ Multi-signal relevance classifier enforces domain safety constraints on every retrieved document.\newline
  \textbullet\ Web-based configuration enables domain adaptation without modifying source~code. \\
  \hline
  \vspace{-6ex}General-Purpose Frameworks~\cite{chase2023langchain,liu_llamaindex_2022,llamaindex_workflows_2025,greyling2024llamaindex} &
  \textbullet\ Adapting to a new domain requires writing retrieval code, redefining prompt logic, and~managing deployment infrastructure.\newline
  \textbullet\ No built-in administration console for non-programmer domain experts.\newline
  \textbullet\ No saved-state portability for deterministic deployment across environments.\newline
  \textbullet\ No SSE-based real-time pipeline feedback to end users. &
  \textbullet\ Zero-code web interface exposes all configuration (prompts, retrieval, UI, environment) without touching source code.\newline
  \textbullet\ Saved-state export bundles all configuration into a single redeployable snapshot.\newline
  \textbullet\ SSE streaming communicates pipeline stage progress to the user in real~time. \\
  \hline
  \vspace{-6ex}Autonomous Multi-Agent Systems~\mbox{\cite{SignificantGravitas_AutoGPT_2023,Moura_CrewAI_2024,langchain_odr_2025,dang2025multi,zhang2025osc}} &
  \textbullet\ Retrieve from the open web with no quality filters on source credibility, domain appropriateness, or~regulatory compliance.\newline
  \textbullet\ No mechanism for a domain owner to lock in curated data sources or safety rules without modifying code.\newline
  \textbullet\ Not designed for expert-governed, hallucination-minimizing applications. &
  \textbullet\ Domain-specific data sources (PubMed, Stack Overflow, arXiv, news APIs) are configured explicitly, not inferred at runtime.\newline
  \textbullet\ Relevance classifier applies domain safety constraints (e.g., exclude animal-only studies in medical domains).\newline
  \textbullet\ All safety rules and data sources are locked in via saved~state. \\
  \hline
  \vspace{-4ex}Domain-Specific Expert QA~\cite{dietnerd2024,hu2025agentic,bioengineering12080895,li2025lexrag,li2025medrgag,yang2025dsrag, jiang2025argus} &
  \textbullet\ Achieve high answer quality within one domain but require code-level changes to deploy in any other field.\newline
  \textbullet\ Domain-specific behavior is hardcoded rather than configurable.\newline
  \textbullet\ Cannot be repurposed by non-programmers or domain experts without developer involvement. &
  \textbullet\ \textls[-15]{Any domain is instantiated through configuration \mbox{alone---prompts,}} retrieval code, UI branding, and~environment variables---without code changes.\newline
  \textbullet\ All domain behavior is exportable as a named saved state and loadable on any \NerdEngine instance by non-programmers. \\
  \hline
  \end{tabularx}
  \normalsize
  \end{table}
  
\vspace{-6pt}

  \begin{table}[H]

  \caption{Design comparison: \NerdEngine versus LangGraph-style agentic workflows (OpenDeepResearch in Section~\ref{sec:NerdEngine_evaluation}).}
  \label{tab:nerdengine-vs-langgraph}
  \small
  \renewcommand{\arraystretch}{1.2}
  \setlength{\tabcolsep}{3pt}
  \begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{2.6cm} >{\raggedright\arraybackslash}X >{\raggedright\arraybackslash}X@{}}
  \toprule
  \textbf{Dimension} & \textbf{\NerdEngine} & \textbf{LangGraph Agentic Workflow} \\
  \midrule
  Architecture & Configuration-first, domain-specialized nine-stage pipeline & General-purpose supervisor--researcher--writer--critic~graph \\
  Control model & Deterministic saved-state bundle & Dynamic graph traversal; critic may trigger revision~loops \\
  Early filtering & Relevance classifier before synthesis & Full retrieved set passed to writer~node \\
  Concurrency & Parallel relevance scoring and batch summarization & Mostly sequential node~execution \\
  LLM calls per query & Bounded, versioned prompts & Variable (extra rounds when critic rejects) \\
%  Prompt growth & Fixed, versioned prompts per stage & Grows with retrieved context passed to~writer \\
%  Token efficiency & Lower (pre-synthesis pruning) & Higher (full retrieval set in synthesis) \\
%  Cost predictability & High (fixed pipeline) & Moderate (depends on critic revision loops) \\
%  Evidence access & Curated JSONL + Stack Overflow API; temporal cutoff enforced & Same curated JSONL + Stack Overflow API; same temporal cutoff (Appendix~\ref{OpenDeepResearchmods}) \\
%  Deployment & Web UI + saved state; no graph coding & Requires graph construction and node orchestration in~code \\
%  Primary optimization & Bounded cost, scorable rate, domain safety gates & Flexible multi-step reasoning and grounding~depth \\
  \bottomrule
  \end{tabularx}
  \normalsize
  \end{table}
  
  
 \begin{table}[H]\ContinuedFloat  

  \caption{\emph{Cont.}}
  \label{tab:nerdengine-vs-langgraph}
  \small
  \renewcommand{\arraystretch}{1.2}
  \setlength{\tabcolsep}{3pt}
  \begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{2.6cm} >{\raggedright\arraybackslash}X >{\raggedright\arraybackslash}X@{}}
  \toprule
  \textbf{Dimension} & \textbf{\NerdEngine} & \textbf{LangGraph Agentic Workflow} \\
  \midrule
%  Architecture & Configuration-first, domain-specialized nine-stage pipeline & General-purpose supervisor--researcher--writer--critic~graph \\
%  Control model & Deterministic saved-state bundle & Dynamic graph traversal; critic may trigger revision~loops \\
%  Early filtering & Relevance classifier before synthesis & Full retrieved set passed to writer~node \\
%  Concurrency & Parallel relevance scoring and batch summarization & Mostly sequential node~execution \\
%  LLM calls per query & Bounded, versioned prompts & Variable (extra rounds when critic rejects) \\
  Prompt growth & Fixed, versioned prompts per stage & Grows with retrieved context passed to~writer \\
  Token efficiency & Lower (pre-synthesis pruning) & Higher (full retrieval set in synthesis) \\
  Cost predictability & High (fixed pipeline) & Moderate (depends on critic revision loops) \\
  Evidence access & Curated JSONL + Stack Overflow API; temporal cutoff enforced & Same curated JSONL + Stack Overflow API; same temporal cutoff (Appendix~\ref{OpenDeepResearchmods}) \\
  Deployment & Web UI + saved state; no graph coding & Requires graph construction and node orchestration in~code \\
  Primary optimization & Bounded cost, scorable rate, domain safety gates & Flexible multi-step reasoning and grounding~depth \\
  \bottomrule
  \end{tabularx}
  \normalsize
  \end{table}
  
  \section{\NerdEngine in~Action}
  \label{sec:System_in_action}
  
  \color{black}
  To show how one can set up a new application, we first show how to install the base system, and then we show how to customize the system to a new~application.
  
  \subsection{Setup}
  
  The \NerdEngine code is available at %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”.
% Author response: Access date added below in the standard MDPI format. The repository is maintained by author H.K.; this access date pins the version cited in the published paper.
 \url{https://github.com/Harsh23Kashyap/Custom-Nerd} (accessed on 25 June 2026). CloudNerd evaluation artifacts, saved-state bundles, and~run logs referenced in Section~\ref{sec:NerdEngine_evaluation} and Appendix~\ref{app:eval_run_history} are documented in that repository. Full installation instructions for Windows (WSL), macOS, and~Linux are provided in Appendix~\ref{app:customnerd}.
  
  \paragraph{Switching Between Nerd Variants}
  Once the site is running, a~new NerdEngine Developer (hereafter D) can either start from a blank configuration or reuse one of our preconfigured NerdEngine variants. Click the \textbf{\hl{Configurations} %MDPI: Please confirm if the bold formatting is necessary; if not, please remove it. The following highlights are the same. RESPONSE: Please leave as is throughout.
} button in the navigation bar and select \textbf{\hl{Load Saved States.}} This opens a panel listing the saved states \NerdEngine ships by default: \hl{\textbf{DietNerd},} %MDPI: RESPONSE: Please leave as is.
 \hl{\textbf{SpaceNerd},} \hl{\textbf{CloudNerd},} \hl{\textbf{SciNerd},} and~\textbf{\hl{NewsNerd} %MDPI: RESPONSE: Please leave as is.
} (visual examples of each variant's home page are shown in Appendix~\ref{app:web_interface_variants}). D can load any of these as-is, or~treat them as templates that D customizes and saves under a new name. After~choosing a state, D can click \hl{\textbf{Load},} refresh the page, and~then return to the \textbf{\hl{Home} %MDPI: RESPONSE: Please leave as is.
} tab---the selected Nerd will now be the default behavior for the site. Appendix~\ref{app:web_interface_saved_states} provides a detailed visual walkthrough of the saved state management~interface. 
  
  Note that D must provide the  API keys, when necessary, for~each application's data sources (e.g., OpenAI, Gemini, or~Claude keys for model access, PubMed or news APIs where applicable) in the environment configuration (see Appendix~\ref{app:web_interface_environment}).  \hl{\textbf{Ollama},}  requires no API key and runs entirely on  a local machine. Selecting \texttt{LLM=Ollama} instead triggers an automated local setup workflow (see Section~\ref{sec:deploy_alt_llm}).
  
  
  \color{black}
  
  \subsection{Customization---An~Example}
  
  To illustrate how \NerdEngine can be deployed on a new application through configuration alone, we present a detailed walkthrough of \hl{\textbf{CloudNerd},} a~specialized assistant for cloud infrastructure troubleshooting. This case study demonstrates the complete configuration process, from~initial requirements to production deployment, achieved entirely through the web interface {\em \hl{without code modifications to the core system.} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 A~comprehensive visual guide to the web interface is provided in Appendix~\ref{app:web_interface}. 
  
  \subsubsection{Domain Requirements and Design~Goals}
  The CloudNerd example application of \NerdEngine aims to serve as a technical troubleshooting assistant for cloud engineers and DevOps practitioners. The~target domain spans cloud computing platforms (AWS, Azure, GCP, Oracle Cloud, DigitalOcean), containerization technologies (Docker, Kubernetes), infrastructure-as-code tools (Terraform, Pulumi, ARM templates, CloudFormation), serverless computing (Lambda, Azure Functions, Cloud Run), cloud networking (VPC, load balancers, CDN, DNS), and~security practices (IAM, RBAC, compliance, encryption).
  
  %Unlike the medical domain which prioritizes peer-reviewed research for evidence quality, 
  CloudNerd takes its expertise primarily from community-validated knowledge on Stack Overflow, where accepted answers, high vote counts, and~author reputation serve as quality signals. The~design prioritizes practical, reproducible solutions with code snippets and configuration examples over theoretical discussions. The~extended saved state \texttt{saved\_states/CloudNerd Advanced/} augments this with \textbf{\hl{additional cloud-native sources:}} %MDPI: RESPONSE: Please leave as is.
 the AWS official blog RSS feed (plus optional AWS site search and topic-scoped resource pages for services such as Lambda and container platforms), the~Microsoft Azure blog RSS feed together with HTML search-result link harvesting, and~the CloudFest industry conference RSS feed. Candidate URLs are deduplicated, non-article listing hubs are skipped, and~article pages are fetched in parallel; visible text is normalized into the same record shape used for Stack Overflow answers so downstream relevance classification, extraction, and~synthesis need no structural changes. Shared \texttt{helper\_functions.py} logic (enabled by default, configurable via \texttt{RATE\_LIMIT\_CURL\_RESILIENCE}) can re-fetch rate-limited HTTP URLs with \texttt{curl}; the Advanced \texttt{user\_search\_apis.py} adds in-module retries, \texttt{Retry-After} handling, API \texttt{backoff} compliance, and~defensive JSON parsing for Stack Exchange~responses.
  
  \subsubsection{Frontend Configuration: Visual~Identity}
  The visual identity (site name, icon, colors, disclaimer) was set via the Frontend Configuration tab; the full CloudNerd branding settings are provided in Appendix~\ref{app:cloudnerd_config}.
  
  \subsubsection{Backend Configuration: Comprehensive Prompt~Engineering}
  All five prompt categories were  customized for technical troubleshooting through the Backend Configuration tab (see Appendix~\ref{app:web_interface_backend}). Table~\ref{tab:prompt_config} outlines the prompt categories and their~purposes.
  
  \begin{table}[H]
  
  \caption{AI Prompt Configuration~Categories}
  \label{tab:prompt_config}
  \footnotesize
  \renewcommand{\arraystretch}{1.3}
  \begin{tabularx}{\textwidth}{p{3.5cm}X}
  \toprule
  \textbf{Prompt Category %MDPI: RESPONSE: Please leave as is. 
} & \textbf{Purpose and Configuration Options} \\
  \midrule
  \textbf{\hl{Question Validation} %MDPI: Please confirm if the bold formatting is necessary; if not, please remove it. The following highlights are the same.RESPONSE: Please leave as is.
} & 
  Classify whether questions fall within the domain's scope and capabilities. Filters out inappropriate queries before processing~begins. \\
  \midrule
  \textbf{\hl{Query Generation} %MDPI: RESPONSE: Please leave as is.
} & 
  Transform user questions into optimized search queries. Options: simple query generation, query expansion (multiple variations), contention-based queries for academic~domains. \\
  \midrule
  \textbf{\hl{Relevance Classification} %MDPI: RESPONSE: Please leave as is.
} & 
  Evaluate articles for topical fit (relevance to question) and safety value (domain appropriateness). Ensures that only  pertinent and high-quality content proceeds to~synthesis. \\
  \midrule
  \textbf{\hl{Article Processing} %MDPI: RESPONSE: Please leave as is.
} & 
  Distinguish source types (questions vs. answers, studies vs. reviews). Extract structured information: problem context, solutions, risks, benefits, tools, metrics. Generate type-appropriate~summaries. \\
  \midrule
  \textbf{\hl{Response Synthesis} %MDPI: RESPONSE: Please leave as is.
} & 
  Compose final answers with proper citations, domain-specific disclaimers, and~domain-appropriate structure. Governs tone, formality, and~communication~style. \\
  \bottomrule
  \end{tabularx}
  %\normalsize
  \end{table}
  
  The detailed configuration for each prompt category, including domain topic lists, rejection rules, example classifications, and~extraction fields, is provided in Appendix~\ref{app:cloudnerd_config}.
  
  
  
  \subsubsection{Environment Configuration: API Keys and Provider~Selection}
  CloudNerd uses OpenAI as the LLM provider (\texttt{LLM="OpenAI"}) and requires \texttt{OPENAI\_API\_KEY} plus an optional \texttt{STACK\_API\_KEY} that raises the Stack Exchange rate limit from 300 to 10{,}000 requests per day; see Appendix~\ref{app:web_interface_environment}. The~Advanced retrieval bundle does not require separate API keys for the vendor blog RSS or CloudFest feed (public HTTP/RSS); optional \texttt{STACK\_API\_KEY} remains important because Stack Overflow evidence is still retrieved first and capped by \texttt{answer\_counter}, while cloud feed breadth is tuned by \texttt{cloud\_links\_per\_source} and an internal cap on unique cloud~URLs.
  \subsubsection{User Flow Configuration: Retrieval~Implementation}
  The User Flow Configuration tab (see Appendix~\ref{app:web_interface_user_flow}) provides toggle controls for enabling various search features and interaction capabilities. Each option includes an inline SVG diagram (from \texttt{user-flow-diagrams.js}) comparing toggle-off versus toggle-on behavior on the question page or in the retrieval~pipeline.

  When \emph{\hl{Deep Search} %MDPI: RESPONSE: Please leave as is.
} is enabled, article collection is routed through \texttt{tiered\_collect\_ ~~articles()} in \texttt{helper\_functions.py} rather than a single \texttt{collect\_articles()} call. The~active saved state selects a \textbf{\hl{retrieval profile} %MDPI: RESPONSE: Please leave as is.
} (\texttt{RETRIEVAL\_NERD\_PROFILES}); CloudNerd and DietNerd register \texttt{mode: "cascade"} profiles, while other shipped Nerds default to legacy single-pass retrieval. The~user-flow toggle (\texttt{cascade\_retrieval.visible} in \texttt{user\_env.js}) must be \texttt{true} for cascade mode to activate---otherwise the pipeline stays on legacy search even when a cascade profile is~loaded.

  Tiered retrieval runs up to five \textbf{\hl{planes} %MDPI: RESPONSE: Please leave as is.
} in order, merging and deduplicating results until quality gates pass or all enabled planes complete: \textbf{\hl{P1} %MDPI: RESPONSE: Please leave as is.
} precision queries from \texttt{clean\_query()}; \textbf{\hl{P2} %MDPI: RESPONSE: Please leave as is.
} relaxed title/keyword variants; \textbf{\hl{P3} %MDPI: RESPONSE: Please leave as is.
} domain-facet queries (CloudNerd lexicon or DietNerd \texttt{[Humans]} suffix); \textbf{\hl{P4} %MDPI: RESPONSE: Please leave as is.
} broad LLM-expanded queries; \textbf{\hl{P5} %MDPI: RESPONSE: Please leave as is.
} gap-fill for missing key terms. On~Stack Overflow deployments, P1 maps to strict search, P2 to title-only search, and~P4 to extended-keyword expansion; P3 adds a domain facet before broad expansion. The~Deep Search diagram shows a simplified three-step ladder (Strict $\rightarrow$ Title $\rightarrow$ Extended); plane budgets and short-circuit behavior are code-defined, not set in \texttt{variables.env} \mbox{(Appendix~\ref{app:cloudnerd_eval_pipeline}).}
  
  %The most technically complex aspect of the CloudNerd configuration involved implementing the retrieval logic that integrates the Stack Exchange API. This logic was authored in the User Flow Configuration tab using the platform’s embedded code editor.
  
  %\textbf{Implementation Architecture.}
  The retrieval layer in \texttt{CloudNerd Advanced} combines (i) a \textbf{\hl{two-stage Stack Exchange} %MDPI: RESPONSE: Please leave as is.
} pipeline optimized for Stack Overflow’s data model---search for candidate questions per expanded query, then fetch top-voted answers with the \texttt{withbody} filter---with (ii) a \textbf{\hl{parallel cloud RSS and page-fetch} %MDPI: RESPONSE: Please leave as is.
} stage that queries AWS, Azure, and~CloudFest feeds (and related search or resource-hub links where configured), deduplicates URLs, and~extracts article-visible text into Stack-compatible records. Stack results are returned first, followed by up to a capped number of cloud-sourced items derived from all expanded~queries.
  
  The number of Stack Overflow answers is configurable via \texttt{answer\_counter}; cloud breadth per provider uses \texttt{cloud\_links\_per\_source}, with~an additional cap on total unique cloud URLs merged into the~batch.
  
  The complete retrieval implementation, including RSS matching with query-token expansion, API interaction, deduplication, rate limiting and resilience, HTML normalization, and~metadata extraction, is provided in Appendix~\ref{app:stackoverflow_retrieval_code}. 
  
  
  \subsubsection{User Interface Configuration for Search Strategies on~CloudNerd}
  PDF upload and identifier-based search were disabled; Stack Exchange article search was enabled by default with references, chat history, query cleaning, and~Deep Search (\texttt{cascade\_retrieval.visible}) active (see Appendix~\ref{app:cloudnerd_config}).
  
  \subsubsection{Query Refinement~Pipeline}
  
  \textls[-35]{Query expansion outputs are post-processed by \texttt{clean\_query(query\_list, max\_queries =None)}} in \texttt{clean\_query.py}, which parses JSON \texttt{expanded\_queries} (or plain nested lists) into search strings suitable for the Stack Exchange~API.
  
  The  preprocessing function used to parse and sanitize expanded queries is provided in Appendix~\ref{app:query_refinement_code}.
  
  
  \subsubsection{Saved State: Complete Configuration~Bundle}
  
  After testing, the~complete configuration was exported as a named saved state. The~repository includes a baseline \texttt{saved\_states/CloudNerd/} bundle and an extended \texttt{saved\_states/CloudNerd Advanced/} bundle (multi-source retrieval as above); each bundles prompts, UI settings, retrieval code, environment variables, and~logo. The~full file listing for the CloudNerd configuration walkthrough is provided in Appendix~\ref{app:cloudnerd_config}; loading a chosen state on any \NerdEngine instance deterministically restores that CloudNerd deployment~variant.

\textls[-35]{  The evaluated CloudNerd configuration used in Section~\ref{sec:NerdEngine_evaluation} replaces advanced multi-source RSS retrieval with multi-plane tiered Stack Overflow retrieval (\texttt{tiered\_collect\_articles}:} planes P1--P5; benchmark runs map P1 to strict search, P2 to title-only, and~P4 to extended-keyword expansion), activated by the CloudNerd code profile plus the User Flow \emph{\hl{Deep Search} %MDPI: RESPONSE: Please leave as is.
} toggle, post-matcher TF-IDF citation recovery, supplemental gap-fill when coverage is thin, and~an evidence-constrained synthesis prompt (Appendix~\ref{app:cloudnerd_eval_pipeline}).

  \subsubsection{CloudNerd~Question-Answering}
  
  To demonstrate CloudNerd in action, consider how the system processes the question ``How to secure a Kubernetes cluster?'':
  
  \textbf{\hl{Step 1: Question Validation} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
      \item Input: ``How to secure a Kubernetes cluster?''
      \item Prompt: \texttt{DETERMINE\_QUESTION\_VALIDITY\_PROMPT} (Appendix %MDPI: There is no such section in the text. Please check and provide the correct citation.
% Author response: The label \texttt{app:cloudnerd_question_validity} does exist in the appendix at line 3080 (CloudNerd Question Validity Prompt subsection within Appendix~\ref{app:cloudnerd_eval_prompts}). The reference resolves correctly when the document is fully compiled with \texttt{pdflatex} + \texttt{bibtex} + \texttt{pdflatex} + \texttt{pdflatex}. No change needed; the citation is correct.
~\ref{app:cloudnerd_question_validity});
      \item Output: \texttt{"True"} (Kubernetes is a recognized cloud technology);
      \item Action: Proceed to query generation.
  \end{itemize}
  
  \textbf{\hl{Step 2: Query Expansion} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
      \item Input: ``How to secure a Kubernetes cluster?''
      \item Prompt: \texttt{GENERAL\_QUERY\_PROMPT} (Appendix %MDPI: There is no such section in the text. Please check and provide the correct citation. Author response: The label \texttt{app:cloudnerd_query_generation} does exist in the appendix at line 3208 (CloudNerd Query Generation Prompt subsection within Appendix~\ref{app:cloudnerd_eval_prompts}). The reference resolves correctly on full compilation. No change needed.
~\ref{app:cloudnerd_query_generation});
      \item Output: Twelve JSON-formatted variations covering RBAC, network policies, pod security, TLS certificates, API server security, authentication, authorization, secrets management, and~monitoring;
      \item Action: Pass to query refinement.
  \end{itemize}
  
  \textbf{\hl{Step 3: Query Refinement} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
      \item Input: JSON object with \texttt{expanded\_queries} array;
      \item Function: \texttt{clean\_query(query\_list, max\_queries=None)} in \texttt{clean\_query.py};
      \item Output: List of 12 plain-text queries (feeds plane~P1 when Deep Search is on);
      \item Action: Execute retrieval---\texttt{CloudNerd Advanced} uses Stack Overflow plus cloud RSS/page fetch per query; the 120-question benchmark uses tiered Stack Overflow retrieval instead (Appendix~\ref{app:cloudnerd_eval_pipeline}).
  \end{itemize}

  \textbf{\hl{Step 4: Article Retrieval} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
      \item Input: Twelve query variations (and, when Deep Search is enabled, additional plane~P2--P5 query batches);
      \item \textls[-35]{Function: \texttt{collect\_articles} in \texttt{user\_search\_apis.py}, orchestrated by \texttt{tiered\_collect \_articles()}} when \texttt{cascade\_retrieval.visible} is \texttt{true};
      \item Process (advanced saved state): For each query, search Stack Overflow for up to 10~questions, fetch up to five answers per question (votes-sorted), deduplicate by question ID, clean HTML while preserving code blocks; then gather AWS blog RSS, Azure blog RSS (and blog search links where applicable), and~CloudFest RSS hits with token-based query expansion, merge and deduplicate URLs, skip search-hub pages, and~parallel-fetch article HTML into plain text capped for token budget;
      \item Process (benchmark eval): Planes P1--P2 run strict and title-only Stack Overflow searches; plane~P3 adds domain-facet queries; planes P4--P5 add broad expansion and term gap-fill when quality gates do not short-circuit;
      \item Output: Up to \texttt{answer\_counter} Stack Overflow--shaped records, followed in the Advanced bundle by a capped set of cloud-sourced records (same schema: title, URLs, body text) for unified downstream processing.
  \end{itemize}
  
  \textbf{\hl{Step 5: Relevance Classification} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
      \item Input: Merged evidence batch (Stack Overflow answers plus optional vendor or industry feed excerpts);
      \item Prompt: \texttt{RELEVANCE\_CLASSIFIER\_PROMPT} (Appendix %MDPI: There is no such section in the text. Please check and provide the correct citation.
% Author response: The label \texttt{app:cloudnerd_relevance} does exist in the appendix at line 3446 (CloudNerd Relevance Classifier Prompt subsection within Appendix~\ref{app:cloudnerd_eval_prompts}). The reference resolves correctly on full compilation. No change needed.
~\ref{app:cloudnerd_relevance}; executed in parallel)
      \item Output: Binary classifications (yes/no) for each item;
      \item Action: Filter to retain only ``yes'' items (typically dozens remain depending on caps).
  \end{itemize}
  
  \textbf{\hl{Step 6: Information Extraction} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
      \item Input: Relevant evidence items (Q\&A or blog-style bodies);
      \item \textls[-15]{Prompts: \texttt{ARTICLE\_TYPE\_PROMPT} (classify as question/answer), \texttt{ABSTRACT\_EXTRACTION \_PROMPT}} (extract 13 structured fields);
      \item Output: Structured summaries, including problem context, proposed solutions, accepted fixes, risks, benefits, cloud services, tools, configurations, performance metrics, errors, security considerations, alternatives, and~community signals (where applicable);
      \item Action: Pass structured data to synthesis.
  \end{itemize}
  
  \textbf{\hl{Step 7: Answer Synthesis} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
      \item Input: Structured summaries from the retained evidence set;
      \item Prompt: \texttt{FINAL\_RESPONSE\_PROMPT} (Appendix %MDPI: There is no such section in the text. Please check and provide the correct citation.
% Author response: The label \texttt{app:cloudnerd_final_response} does exist in the appendix at line 3918 (CloudNerd Final Response Prompt subsection within Appendix~\ref{app:cloudnerd_eval_prompts}). The reference resolves correctly on full compilation. No change needed.
~\ref{app:cloudnerd_final_response});
      \item Process: Select 8--20 highest-quality items (prioritizing accepted Stack Overflow answers and high scores where present), organize evidence into thematic sections (e.g., Access Control and Identity Management, Network Policies and Encryption, Audit and Monitoring), explain RBAC setup, network policy configuration, TLS encryption, and~monitoring tools (Falco, kube-bench) when supported by sources, include security warnings about misconfiguration risks, format citations with URLs (Stack Overflow and vendor or feed pages as appropriate);
      \item Output: Comprehensive technical answer with inline citations.
  \end{itemize}
  
  The complete workflow executes in approximately 30--60 s, balancing thorough evidence collection with acceptable latency for technical~users.
  
  \subsubsection{Configuration Time~Investment}
  
  The entire CloudNerd configuration was completed in approximately 2.5 h using the web interface configuration tabs (see Appendix~\ref{app:web_interface}):
  \begin{itemize}
      \item \textbf{\hl{Frontend~(15~min):} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 Set branding elements, colors, disclaimer (see Appendix~\ref{app:web_interface_frontend});
      \item \textbf{\hl{Environment~(5~min):} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 Add API keys, select OpenAI provider (see Appendix~\ref{app:web_interface_environment});
      \item \textbf{\hl{User~Flow~(45~min):} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 Write \texttt{collect\_articles} function, configure HTML cleaning, test Stack Exchange API integration (see Appendix~\ref{app:web_interface_user_flow});
      \item \textbf{\hl{Prompts~(80~min):} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 Customize all five prompts with cloud-specific rules, examples, and~extraction fields (see Appendix~\ref{app:web_interface_backend});
      \item \textbf{\hl{Testing~(15~min):} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 Validate with sample questions (Kubernetes security, EC2 selection, database migration);
      \item \textbf{\hl{State~Saving~(2~min):} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 Export complete configuration bundle (see Appendix~\ref{app:web_interface_save_state}).
  \end{itemize}
  
  This 2.5-h investment produced a production-ready cloud troubleshooting assistant without any modifications to \NerdEngine's core codebase, demonstrating the framework's configuration-first design philosophy and the power of prompt-driven domain~specialization.
  
  
  
  
  \color{black}
  % --------------------------------------------------------
  %\section{\NerdEngine Case Study: Domain Configuration, Implementation, and Strategic Differentiation}
  \section{\highlighting{Materials and~Methods} %MDPI: This is a reminder: The companies/manufacturers of chemicals & reagents, devices, instruments, commercial cell lines/samples/materials should be indicated together with their city (states abbreviation is required for USA and Canada) and country in their first appearance. Please add necessary information for those items in text. For all the software used, please add the information of them. RESPONSE: Not applicable for this computer science paper
}
  \label{sec:customnerd_case}
  
  
  \textbf{\NerdEngine} is designed to be a generator for expertise-based  retrieval-augmented generation (RAG) applications. %\NerdEngine unifies information ingestion, filtering, evaluation, and~synthesis into a reproducible, domain-tailorable architecture. %In contrast to single-domain assistants, \NerdEngine serves as  a configurable engine that can instantiate multiple expertise-supported systems with minimal engineering overhead. 
  Because \NerdEngine externalizes domain-specific behavior into metadata
  and prompt definitions, it can be tailored to support any application that for which there is  a reliable expert literature. This paper illustrates this generalizability with four distinct examples:   a medical research
  assistant, a~news summarizer, a~cloud-technology advisor, and~a space-science~explainer. 
  
  The \NerdEngine framework has the following three core features as~goals:
  \begin{enumerate}
      \item \textbf{\hl{Consistency} %MDPI: RESPONSE: Please leave as is.
}---similar developer experience across domains.
      \item \textbf{\hl{Adaptability} %MDPI: RESPONSE: Please leave as is.
}---reconfiguration without code changes.
     \item \textbf{\hl{Domain Fidelity} %MDPI: RESPONSE: Please leave as is.
}---answers that reflect the evidence, tone, and~regulatory needs of the domain.
  \end{enumerate}
  
  These features enable new verticals to be deployed in hours rather than~months.
  
  % -----------------------------------------------------------------
  
  \subsection{Algorithm~Overview}
  
  While the details of an application assistant  depend on specific prompts and database accesses, the~framework consists of the basic steps  shown diagrammatically in Figure~\ref{fig:decisiontree}. Algorithm~\ref{textalgorithm} shows the method in pseudo-code, along with references to the corresponding sections for a detailed description of those steps. The~pipeline generalizes the DietNerd methodology~\cite{dietnerd2024} to multiple domains. %\textbf{\textcolor{red}{TBD --- cite DietNerd paper and section when generalizing from that methodology.}} \vspace{12pt}
  
    \begin{figure}[H]
  
  \begin{adjustwidth}{-\extralength}{0cm}
  \centering
     \hspace*{10mm} \scalebox{0.8}[0.8]{\begin{tikzpicture}[node distance=2cm, auto]
          \hspace{-2em}
          % Local style definitions to ensure standalone compile
          \tikzstyle{root}=[rectangle, draw, fill=black!10, text width=5em, text centered, rounded corners, minimum height=4em]
          \tikzstyle{decision}=[diamond, draw, fill=purple!10, text width=2.5cm, text centered, node distance=3cm, inner sep=0pt]
          \tikzstyle{terminal}=[rectangle, draw, fill=black!5, text width=6.2em, text centered, rounded corners, minimum height=3em]
          \tikzstyle{action}=[circle, draw, fill=green!10, minimum size=1.0cm]
          \node (root) [root] {User Question};
          \node (dec1) [decision, right of=root, xshift=0.75cm] {Query already answered?};
          \node (out1) [terminal, below of=dec1, yshift=-0.75cm] {Pre-generated Answer + Citation};
          \node (dec2) [decision, right of=dec1, xshift=1.5cm] {Is the question valid?};
          \node (out2) [terminal, below of=dec2, yshift=-0.75cm] {Canned Response \\ Declining to Answer};
          \node (act1) [terminal,action, right of=dec2, xshift=1cm] {Article Retrieval};
          \node (dec3) [decision, right of=act1, xshift=1cm] {Is the article relevant?};
          \node (act2) [terminal,action, right of=dec3, xshift=1.5cm] {Article Analysis\\+\\Summary};
          \node (act3) [terminal,action, below of=dec3, yshift=-1cm] {Log +\\Disregard\\Article};
          \node (out3) [terminal, below of=act2,yshift=-0.65cm] {Generated \\ Answer + Citation};
  
          \draw[->] (root) -- (dec1);
          \draw[->] (dec1) -- node {no} (dec2);
          \draw[->] (dec1) -- node {yes} (out1);
          \draw[->] (dec2) -- node {yes} (act1);
          \draw[->] (dec2) -- node {no} (out2);
          \draw[->] (act1) -- node { } (dec3);
          \draw[->] (dec3) -- node {yes} (act2);
          \draw[->] (dec3) -- node {no} (act3);
          \draw[->] (act2) -- node { } (out3);
      \end{tikzpicture}}
  \end{adjustwidth}
      \caption{\highlighting{{\bf{{\textbf{\NerdEngine~{Workflow Diagram}}:}}}} %MDPI: RESPONSE: Please leave as is.
 %MDPI: RESPONSE: Please leave as is.
 A workflow showing the dynamics of \NerdEngine starting with a user question. Purple diamonds are decision points. Green circles are actions. Red squares are outputs. {This diagram is expressed in text in Algorithm \ref{textalgorithm}.}}
      \label{fig:decisiontree}
  \end{figure}
  
  
    \subsection{System Design and~Architecture}
  
  \textbf{\hl{Chain-of-LLMs~and~Data~Integration.} %MDPI: RESPONSE: Please leave as is.
} %MDPI: RESPONSE: Please leave as is.
 \NerdEngine orchestrates multiple LLM prompts to validate questions, generate search queries, evaluate relevance, normalize records, and~synthesize final answers with citations. The~LLM backend---\textbf{\hl{OpenAI} %MDPI: RESPONSE: Please leave as is.
} (GPT series), \textbf{\hl{Gemini} %MDPI: RESPONSE: Please leave as is.
} (Google), \textbf{\hl{Claude} %MDPI: RESPONSE: Please leave as is.
} (Anthropic), or~\textbf{\hl{Ollama} %MDPI: RESPONSE: Please leave as is.
} (local, open-weight models)---is selected via a single \texttt{LLM} configuration variable, with~prompts sourced from the prompt configuration file and helper utilities. Ollama enables fully offline, privacy-preserving operation with no API billing; the system automatically handles installation, model downloading, and~server lifecycle management. Built-in Application Programmer Interfaces (APIs) include, for~example, PubMed and publisher APIs for academic domains, news APIs for current events, Stack Overflow and documentation APIs for cloud, and~arXiv/NASA for space. Other interfaces can be added by implementing the standard retrieval interface in the \texttt{user\_search\_apis.py} file. This module exposes a \texttt{collect\_articles(query\_list)} function that accepts a list of search queries and returns a list of standardized article dictionaries, allowing seamless integration of any new data source into the pipeline without modifying core~logic.
  
  \vspace{12pt}
  
  \begin{algorithm}[H]

  \caption{{\highlighting{\textbf{\NerdEngine~{Algorithm Text}}:}}%MDPI: To reduce the space, we crossed heading 2, please check and confirm. RESPONSE: Confirmed.
   Start from a user question $Q$, as~also illustrated in Figure~\ref{fig:decisiontree}. The~condition on previous question--answer pairs requires database access. The~{de novo} generation of an answer entails several prompts, as illustrated for our four application areas in \cref{question-validity,query-generation,relevance-classifier,full-text-parse,SoC-analysis,output-synthesis} and their corresponding appendices.
  \label{textalgorithm}}

   \If {user determines question $Q$ is similar to some previously answered question $Q'$} 
          {
              User selects $Q'$ \\
              System returns the previously generated answer for $Q'$\\
          
   \Else
          {
              \If {question $Q$ is valid (Sections~\ref{question-validity} and \ref{query-generation})}
              {
                  Parse the user question into keywords (Section~\ref{query-generation})\\
                  Perform article retrieval using configured providers\\
                  \For{Each returned article A}
                  {
                      \If {A is relevant to Q or includes safety considerations (Section~\ref{relevance-classifier})}
                      {
                          Summarize the article in a few sentences \\
                          Perform an article analysis and grade it (Sections~\ref{SoC-analysis} and \ref{full-text-parse})
                      }
                  }
                  Only the strongest (highest grade) articles are used to answer the question (Section~\ref{output-synthesis}).
              }
          }
          }
  \end{algorithm}
    \vspace{12pt}
  

  

  
  
  
  \textbf{\hl{Nucleus sampling and temperature settings.} %MDPI: RESPONSE: Please leave as is.
} Our  framework for configuring sampling parameters based on the task extends the methodology described in the DietNerd paper (Section~3.2,~\cite{dietnerd2024}) %~\footnote{\url{https://www.mdpi.com/journal/electronics}} 
  to support a variety of domain deployments. Our parameter selection strategy is informed by a careful consideration of the trade-offs between sampling diversity and output determinism; the strategy explicitly models how the interplay between temperature and \highlighting{{\em top-$p$}} %MDPI: RESPONSE: Please leave as is.
 governs both coherence and creativity. For~example, a~low temperature in conjunction with a high top-$p$ ($T \in [0.0, 0.3]$, $p \in [0.9, 1.0]$) produces highly deterministic outputs while maintaining lexical diversity~\cite{holtzman_neural_text_degeneration_2019,wiher_decoding_strategies_2022}. In~contrast, a~high temperature with low top-$p$ ($T > 0.7$, $p < 0.7$) can result in more unpredictable token sequences that may lack thematic consistency~\cite{ravfogel_conformal_nucleus_sampling_2023,nguyen_minp_sampling_2025}.
  
  With this in mind, we propose the following parameter-setting guidelines based on domain requirements and task~type: 
  \begin{itemize}
    \item \textbf{\hl{High-precision tasks} %MDPI: RESPONSE: Please leave as is.
} \textls[-25]{(e.g., legal, medical, financial, structured extraction):} \mbox{$T \in [0.0, 0.1]$,} $p \in [0.9, 1.0]$ to minimize hallucinations and maximize reproducibility~\cite{holtzman_neural_text_degeneration_2019,ji_hallucination_nlg_2023}. 
    \item \textbf{\hl{Analytical tasks} %MDPI: RESPONSE: Please leave as is.
} (e.g., literature reviews, summarization, reasoning): $T \in [0.2, 0.4]$, $p \in [0.8, 1.0]$ to balance grounded reasoning with flexible phrasing~\cite{wang_self_consistency_cot_2023,kadavath_language_models_know_2022}.
    \item \textbf{\hl{Classification tasks} %MDPI: RESPONSE: Please leave as is.
} (e.g., validity, relevance, categorical mapping): $T \in [0.0, 0.2]$, $p \in [0.5, 0.9]$ to reduce output variance and improve inter-run reliability.~\cite{lin2022truthfulqameasuringmodelsmimic}
    \item \textbf{\hl{Creative synthesis} %MDPI: RESPONSE: Please leave as is.
} (e.g., rewriting, narrative, marketing): $T \in [0.5, 0.8]$, $p \in [0.8, 1.0]$ to enable diverse word choice while preventing improbable token sampling~\cite{holtzman_neural_text_degeneration_2019,wiher_decoding_strategies_2022}.
  \end{itemize}
  
  These defaults reflect the entropy requirements of each task. Lower temperatures are used for deterministic classification and structured extraction tasks to reduce output variance, while higher temperatures are used in synthesis tasks to enable lexical diversity without sacrificing coherence through nucleus truncation. Using this information, our system implements the following defaults with GPT-4-turbo and Gemini-1.5-Pro. Question validity checks use low-entropy decoding ($T = 0.2$, $p = 0.7$) to ensure consistent binary judgments. Section mapping  uses near-deterministic decoding ($T \in [0.0, 0.2]$, $p = 0.9$) because the task requires selecting the most probable structural mapping rather than generating novel text. Relevance judgments allow slightly higher stochasticity ($T \in [0.3, 0.5]$, $p = 0.8$) to capture alternative phrasing patterns that may signal semantic similarity. For~CloudNerd evaluation, final synthesis uses low temperature ($T = 0.2$, $p = 1.0$, \texttt{FINAL\_RESPONSE\_TEMPERATURE=0.2}) to prioritize grounded, reproducible answers over lexical diversity. Other \NerdEngine domains may use higher $T$ for creative synthesis per the task-type guidelines above; temperatures above $0.8$ are not recommended except for specialized creative tasks due to the increased risk of semantic drift~\cite{chang2023kldivergenceguidedtemperaturesampling,kadavath_language_models_know_2022}. 
  
  
  % We follow the DietNerd sampling method while accommodating multiple providers.
  % \begin{itemize}
  %   \item \textbf{Nucleus Sampling (top-p):} For nuanced generation (final synthesis, long summaries), use top-p $=1.0$ with GPT-4-turbo or Gemini-1.5. For classification-style tasks (validity, relevance), to reduce lexical variance, set top-p to $ 0.5$.
  %   \item \textbf{Temperature:} Set $T\in[0.5,0.8]$ for generation; use low $T$ for discrete/structured tasks: $T=0.2$ for question validity; $T\in[0.0,0.2]$ for section/identifier mapping; $T\in[0.3,0.5]$ for relevance judgements.
  % \end{itemize}
  
  \subsubsection{Similar Question~Matching}\label{question-match}
  \NerdEngine includes a question matching subsystem that identifies similar past questions from the conversation history, enabling users to obtain previously generated answers without regenerating responses. This feature reduces computational costs, improves response time, and~enhances user experience by surfacing relevant historical~answers.
  
  
  When enabled, the~system automatically maintains a searchable database of all questions and their corresponding answers. As~users interact with the system, each question--answer pair is stored with metadata, including timestamps and session information. When a new question is entered, the~system searches this database to find semantically similar past~questions.
  
  \textbf{\hl{Hybrid Similarity Matching:} %MDPI: RESPONSE: Please leave as is.
}
  The system employs multiple complementary methods to identify similar questions, combining four  techniques to maximize~accuracy:
  \begin{enumerate}
    \item \textbf{\hl{Phrase-level matching:} %MDPI: RESPONSE: Please leave as is.
} Identifies questions with similar word combinations and phrases, capturing semantic relationships even when wording differs slightly.
    \item \textbf{\hl{Character-level matching:} %MDPI: RESPONSE: Please leave as is.
} Ignores typographical variations, word order differences, and~morphological variations that might otherwise prevent matches.
    \item \textbf{\hl{Keyword overlap analysis:} %MDPI: RESPONSE: Please leave as is.
} Finds questions with shared important terms, even when the overall structure differs.
    \item \textbf{\hl{Sequence alignment:} %MDPI: RESPONSE: Please leave as is.
} Compares question structure and word order to identify questions that express the same intent through different phrasing.
  \end{enumerate}
  
  The system combines these methods using a max-pooling strategy, selecting the highest similarity score across all approaches ($S_{final} = \max(S_{tfidf}, S_{fuzzy}, S_{jaccard}, S_{sequence})$). This approach prioritizes recall, ensuring that if a question matches strongly on \emph{any} single dimension (e.g., high keyword overlap despite different word order, or~high character similarity despite typos), it is flagged as a potential match, regardless of how users phrase their~questions.
  
  \textbf{\hl{Intelligent Text Processing:} %MDPI: RESPONSE: Please leave as is.
}
  Before matching, questions undergo  normalization to handle natural language variations. The~system recognizes synonyms (e.g., ``pros'' and ``advantages'', ``cons'' and ``disadvantages''), standardizes terminology (e.g., ``B12'' and ``vitamin B12''), and~normalizes formatting differences. This is achieved through a lightweight Natural Language Processing (NLP) pipeline that applies tokenization, stemming (reducing words to their root form), and~a domain-specific dictionary lookup to map variations to canonical terms. This preprocessing ensures that questions with equivalent meaning but different wording may be~similar.
  
  \textbf{\hl{User Experience:} %MDPI: RESPONSE: Please leave as is.
}
  When a user enters a question, the~system automatically searches their entire personal conversation history, which is stored in a file named \texttt{historical\_answer.json} on their device. The~system displays similar past questions as clickable suggestions, so users can easily see if they have already asked the same or a related question before and directly access the previous answer if desired. Users can either select a previous question whose answer will be immediately viewable or~request the system to generate a new~response.
  
  In addition, users can browse their interaction timeline in chronological order, independent of similarity matching, making it simple to review the sequence of previous questions and answers. Since this is each user's own history file, there is no login mechanism required and no sharing between users; all questions and answers are private to the individual. At~any time, a~user may choose to delete their \texttt{historical\_answer.json} file to erase their~history.
  
  \textbf{\hl{Examples:} %MDPI: RESPONSE: Please leave as is.
}
  The system works across diverse domains, recognizing semantic equivalence regardless of domain-specific terminology:
  
  \begin{itemize}
    \item \textbf{\hl{Medical domain:} %MDPI: RESPONSE: Please leave as is.
} As a consequence of  synonym detection, suppose a user  asks, ``What are the pros and cons of vitamin B12 supplementation?'' The system recognizes this as similar to a previous question, ``What are the advantages and disadvantages of taking vitamin B12?'' despite different wording (as noted above, ``pros'' is recognized as a synonym of ``advantages'', and``cons'' with ``disadvantages''). Thus, the prior question immediately surfaces as a~suggestion.
    
    \item \textbf{\hl{Cloud technology domain:} %MDPI: RESPONSE: Please leave as is.
} A user queries, ``How do I secure a Kubernetes cluster?'' The system matches this to a previous question, ``What's the best way to secure Kubernetes?'' recognizing that both seek security best practices for Kubernetes, even with different phrasing. The~existing answer with security recommendations and references is instantly~available.
    
    \item \textbf{\hl{Space science domain:} %MDPI: RESPONSE: Please leave as is.
} A question, ``What causes black holes to form?'' is matched to ``How are black holes created?'' The system understands that ``causes to form'' and ``created'' express the same scientific inquiry, surfacing the previous detailed explanation with academic citations.
  \end{itemize}
  
  In each case, the~system identifies semantic equivalence across different phrasings, allowing users to access complete answers with citations immediately, saving both time and computational~resources.
  
  \subsubsection{Enhancing the Specificity of Responses Through~Questionnaires}
  
  %\textbf{\textcolor{red}{Editorial note: In about a half-page discuss the interface to the questionnaire code (query in-questionnaire out) and then what you did for healthnerd (how you pre-processed Mayo, how you expand the query terms to diseases, and how you choose the 5 to 7 questions based on voting. Show an example with headache or fatigue.}}
  
  If the user asks \NerdEngine to generate an answer as opposed to returning a cached response, application developers may invoke a questionnaire feature to allow users to receive more targeted responses than they would without the questionnaire. This section shows how this works for a medical~application. 
  
  The runtime interface to this module operates on a strict ``query-in, questionnaire-out'' execution pipeline. When a user submits a natural language symptom inquiry (e.g., ``severe headache''), the~system utilizes a lightweight \texttt{gemini-2.5-flash-lite} model to extract canonical clinical keywords, which are subsequently vectorized using Google's \texttt{text-embedding-004}. The~pipeline then computes the cosine similarity via \texttt{sklearn} to map these embedded keywords against our precomputed Mayo Clinic data repository, expanding the single input into a cluster of semantically related differential conditions (e.g., tension headache, migraine, cluster headache, and~sinus headache).
  
  To aggregate and refine the final question set, the~top-ranked condition provides the foundational base questions, while the overlapping features from the remaining related conditions form a collaborative question pool. The~system normalizes heterogeneous question nomenclature (e.g., merging ``smoking status'' and ``smoker'') and applies a max-pooling voting ensemble across the candidate pool, dynamically selecting a highly targeted matrix of 5 to 7 queries with the highest frequency of votes. Furthermore, strict demographic gates are enforced during this selection loop—for instance, automatically excluding neonatal jaundice questionnaires if the verified age profile is adult—effectively adjusting the downstream retrieval scope based on the user's contextual~variance.
  
  For example, for~health applications, we have built a database of diseases, making use of the public Mayo Clinic website, whose landing page is %MDPI: RESPONSE: changed
 \url{https://www.mayoclinic.org/diseases-conditions} (accessed in May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: Done
 Specifically, \NerdEngine uses the selenium library in Python to read the 1117 cataloged diseases in the “Diseases and Conditions" page, focusing on potential risk factors. \NerdEngine uses OpenAI's GPT-4o model to convert the risk factors for each disease into a questionnaire of five questions. Our prompt---shown in \mbox{Appendix \ref{personalization_prompts}---  asks} the LLM  to focus on the most common and most risky factors. The~resulting  questions are stored in a JSON file and presented to the~user.
  
  Given a user's question, the~most related symptom is selected through vector similarity. Embeddings for diseases in the database are created using Google's models/text-embedding-004 model. Receiving an input from the user, \NerdEngine employs a gemini-2.5-flash-lite model to extract \textls[-25]{keywords from the input. These keywords are then embedded with Google's models/text-embedding-004. We use the cosine similarity function in the sklearn library to choose the most related disease in the database, along with the pre-generated~questions.}%The system will use nonredundant questions from the next most similar disease, if~we ask it to return more than five~questions. 
  
  Sometimes a symptom can be sex- or age-restricted. For~example, males do not suffer from ovarian cancer, and~only infants will have neonatal jaundice. %Therefore, if~a user's question indicates ovarian cancer, \NerdEngine will ask the user's sex. 
  Neonatal jaundice questions will then be excluded, for~example, if~the age of interest is not very young. %However, the~demographic questionnaire will not appear if the user directly mentions a symptom. For~example, given the input "what is a recommended diet for morning sickness", even though "morning sickness" is restricted to females, the~demographic questionnaire will not appear, since there is no need to exclude "morning~sickness".
  
  %The questionnaires are tailored to each query. 
  This entire procedure enables a final questionnaire tailored to the user's query. For~example, in~contrast to the questionnaire for the query ``what are diets recommended if I have a headache'', the~query ``what are diets recommended if I'm light-headed'' yields a questionnaire asking about age, birth sex, history of dizziness, family history of balance problems, and~whether the querier takes certain medicines. %Compared with headaches, this new questionnaire wants to judge whether there’s dizziness or relative symptoms, rather than~migraines.
  
  %Given a query, the system offers queriers an option to fill in a questionnaire after they pose the query. The questionnaire will help refine our response.
  
  %Considering two users. User 1 is a 40-45 year old female who smokes, has long-term health conditions, and consumes a diet high in sugar and fat.  User 2 is a female under 35 who has a history of past miscarriages, uterine or cervical problems, and a family history of heart disease.
  
  As an  example of how different answers to the same questionnaire can affect the system's answer, consider the question ``Risk Factors for Miscarriage''.  For~User 1, who is 40--45 years old, smokes, and~has long-term conditions, the~analysis focuses on age and smoking. The~advice consequently centers on the statistical risks of advanced maternal age and the known impacts of smoking. The~"long-term conditions" are treated as a secondary but relevant factor. See Appendix \ref{personalization_outcomes}  for the detailed~response.
  
  In contrast, for~ User 2, who is under 35 with a past miscarriage and uterine problems, the~analysis identifies ``under 35'' as a lower-risk category. Instead, the~questionnaire focuses on  ``Past Miscarriages: yes'' and ``Uterine Or Cervical Problems: yes.'' The advice focuses on  managing physiological and historical medical risks, highlighting the significance of prior pregnancy outcomes and structural health issues. Please find this response also in Appendix \ref{personalization_outcomes}.
  
  %This pattern continues in the "Suggestions for Heart Disease" section. For a User 1, who smokes and maintains a high sugar and fat diet, the recommendations are corrective and action-oriented. The user's "yes" answers to "Smoke" and "Diet With High Sugar Or Fat" are modifiable risk factors. Therefore, the advice is focused on changing these behaviors. For User 2, who has a family history of heart disease but does not smoke or consume a high-fat diet, the recommendations are managerial and preventative. Since this user's lifestyle factors are already positive, \aneme's response centers on family history. This is an unmodifiable risk. As a result, the advice shifts away from "correcting" bad habits and toward proactive management, such as regularly monitoring cardiovascular health and optimizing an already healthy lifestyle as a preventative measure.
  
  
  
  \subsubsection{Question~Validity}\label{question-validity}
  Before processing a question, \NerdEngine employs an AI-powered validation step to ensure the query falls within the domain's scope and capabilities. This gate filters out questions that are outside the system's intended purpose in order to avoid hallucinations because of insufficient information. %preventing wasted computational resources and ensuring users receive appropriate~guidance.
  
  The validation system is domain-configurable, allowing each deployment to define what constitutes a valid question based on its specific focus. The~goal is to reject invalid questions with clear explanations, redirecting users  to appropriate resources when~needed.
  
  \textbf{\hl{Examples across domains:} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
    \item \textbf{\hl{Medical domain:} %MDPI: RESPONSE: Please leave as is.
} Questions about meal planning or recipe requests are flagged as outside scope, as~the system focuses on evidence-based nutritional research rather than meal preparation. Questions about animal nutrition are similarly filtered out, because~the literature source PubMed focuses on human~health.
    
    \item \textbf{\hl{Cloud technology domain:} %MDPI: RESPONSE: Please leave as is.
} Questions about general programming concepts unrelated to cloud infrastructure, or~requests for specific code implementations, are identified as outside the domain's scope. The~system focuses on cloud architecture, security, and~best practices rather than general software~development.
    
    \item \textbf{\hl{Space science domain:} %MDPI: RESPONSE: Please leave as is.
} Questions about astrology, science fiction scenarios, or~speculative theories without a scientific basis are filtered out. The~system maintains focus on peer-reviewed astronomical research and established scientific~knowledge.
    
    \item \textbf{\hl{News domain:} %MDPI: RESPONSE: Please leave as is.
} Questions about historical events beyond recent news cycles, or~requests for opinion pieces rather than factual reporting, are identified as outside the current events focus.
  \end{itemize}
  
  Complete prompt definitions for the domain-specific validation guards are provided in Appendix~\ref{prompt-templates} 
  
  %This validation step ensures that users receive responses aligned with the system's expertise, maintaining quality and relevance while preventing inappropriate use of computational resources.
  
  \subsubsection{Query Generation for Safe~Responses}\label{query-generation}
  After validation for applicability, the~system transforms the user's natural language question into  search queries suitable for domain-specific databases. The~prompt requests a primary search query that captures the core intent of the question, plus optional alternative queries that explore different aspects or potential controversies. This multi-query approach captures both mainstream perspectives and alternative viewpoints when~relevant.
  
  \textbf{\hl{Domain-specific prompt behavior.} %MDPI: RESPONSE: Please leave as is.
} \textls[-15]{Each saved state defines its own generation~strategy:}
  \begin{itemize}
    \item \textbf{\hl{CloudNerd (Stack Overflow).} %MDPI: RESPONSE: Please leave as is.
} \textls[-25]{The `GENERAL\_QUERY\_PROMPT' \mbox{(Appendix %MDPI: There is no such section in the text. Please check and provide the correct citation.
% Author response: Same label as line 723; \texttt{app:cloudnerd_query_generation} exists at line 3208 in the appendix. The reference resolves correctly on full compilation. No change needed.
~\ref{app:cloudnerd_query_generation})}} requires 8--12 JSON-formatted variations under the key `expanded\_queries'.
    
    For example, a~user question such as “How do I secure a Kubernetes cluster?” automatically expands into entries~like:
    \begin{itemize}
      \item “Kubernetes cluster security best practices”;
      \item “Kubernetes role-based access control setup”;
      \item “Kubernetes pod security policies”;
      \item “TLS certificates in Kubernetes cluster”;
      \item “How to monitor Kubernetes security”.
    \end{itemize}
   These variations deliberately cover Role-Based Access Control (RBAC), network segmentation, certificate management, and~observability so that the downstream Stack Overflow collector hits question threads about Identity and Access Management (IAM), NetworkPolicy YAML, Transport Layer Security (TLS) termination, Falco/kube-bench monitoring, etc., without~the end-user needing to enumerate each~vector.
  
    \item \textbf{\hl{SpaceNerd (NASA/Wikipedia).} %MDPI: RESPONSE: Please leave as is.
} Its `GENERAL\_QUERY\_PROMPT' emits newline-separated keywords rather than JSON, because the~NASA Image/Wikipedia APIs favor plain keyword concatenation. For~example, the~request “Show me images of the Andromeda Galaxy” produces the following:
    \begin{itemize}
      \item “Andromeda Galaxy”;
      \item “Andromeda Galaxy photo”;
      \item “Andromeda Galaxy visualization”.
    \end{itemize}
    The first line ensures Wikipedia/ADS lookups, while the subsequent variants target NASA’s media endpoints for photography versus rendered visualizations.
  \end{itemize}
  
  These auto-generated lists are logged alongside the user prompt, enabling transparency (users can see exactly which variants were issued) and reproducibility (saved states guarantee the same set of expansions for the same question).
  
  For domains requiring specialized terminology or query formatting, an~optional query refinement step can perform preprocessing, e.g.,~normalizing technical terms, expanding abbreviations, and~adapting queries to match the conventions of specific databases (e.g., medical terminology for PubMed, technical keywords for cloud documentation). This refinement improves retrieval accuracy, especially for domains with complex or domain-specific~vocabularies.
  
  \subsubsection{Article~Retrieval}\label{article-retrieval}
  The system executes parallel searches across one or more data sources configured for the domain. Each source is queried with the generated search terms, and~results are collected with metadata, including titles, authors, publication dates, identifiers, and~access information. The~system automatically de-duplicates articles that come from multiple sources, ensuring that each unique article is processed only once while preserving all source~attributions.
  
  If users provide specific article identifiers (such as DOI, PMID, or~arXiv IDs), the~system  retrieves those articles in addition to any other search results. This allows users to request analyses of particular papers or documents they already know about. Throughout the retrieval process, progress updates are streamed to the user interface, providing real-time feedback on how many articles have been collected from each~source.
  
  \subsubsection{Relevance Classifier and Safety~Considerations}\label{relevance-classifier}
  Not all retrieved articles are relevant to the user's question. The~system employs an AI-powered relevance classifier that evaluates each article on two dimensions: topical fit (whether the content addresses the question) and safety value (whether the content is appropriate and reliable for the domain). Articles are scored and categorized into relevant and irrelevant groups, with~domain-specific filters applied (e.g., excluding animal-only studies in human health domains, filtering out opinion pieces in evidence-based domains).
  
  Relevance scoring uses a hybrid approach that combines multiple signals to balance precision (keeping results on-topic) and recall (ensuring important articles are included):
  \begin{itemize}
    \item \textbf{\hl{Topical fit:} %MDPI: RESPONSE: Please leave as is.
} Semantic similarity between the article and the user’s question.
    \item \textbf{\hl{Source quality:} %MDPI: RESPONSE: Please leave as is.
} Publication credibility, peer-review status, and~publisher reputation.
    \item \textbf{\hl{Recency:} %MDPI: RESPONSE: Please leave as is.
} Freshness of the material for domains where timeliness matters (e.g., news,~cloud).
    \item \textbf{\hl{Community signals:} %MDPI: RESPONSE: Please leave as is.
} For community-driven sources (e.g., Stack Overflow), upvotes, accepted answers, and~author reputation.
    \item \textbf{\hl{Safety constraints:} %MDPI: RESPONSE: Please leave as is.
} Domain-specific guardrails evaluate articles to filter out unsafe  content, as~we can see in Section \ref{evaluate} below.
  \end{itemize}
  
  Articles are classified in parallel to save time. Then, the~system filters them in multiple steps: an initial broad filter keeps articles that might be relevant, and~later filters use domain-specific rules (for example, removing duplicate news stories or skipping animal-only studies in medical topics). Only articles that meet both relevance and safety standards are kept and moved~forward.
  
  \subsubsection{Evaluation~Pipelines}\label{evaluate}
  
  Evaluation pipelines apply domain-specific filters and validators to ensure that only trustworthy, appropriate content proceeds to synthesis. These pipelines transform raw retrieval results into curated evidence that meets each domain's quality and safety~standards.
  
  \textbf{\hl{Safety and Credibility Filters:} %MDPI: RESPONSE: Please leave as is.
}
  Each domain applies filters tailored to its~requirements:
  \begin{itemize}
    \item \textbf{\hl{Medical domains:} %MDPI: RESPONSE: Please leave as is.
} Enforce peer-review requirements, exclude animal-only studies when human evidence is needed, filter out recipe requests or non-research queries, and~prioritize studies with appropriate sample sizes and statistical rigor.
    \item \textbf{\hl{News domains:} %MDPI: RESPONSE: Please leave as is.
} Filter out opinion-heavy sources, deduplicate articles covering the same event, prioritize multiple-source verification, and~exclude clearly biased or unreliable publications.
    \item \textbf{\hl{Technical domains:} %MDPI: RESPONSE: Please leave as is.
} Highlight security risks and vulnerabilities, prioritize solutions with security best practices, flag deprecated or outdated approaches, and~emphasize cost implications and scalability considerations.
    \item \textbf{\hl{Academic domains:} %MDPI: RESPONSE: Please leave as is.
} Ensure identifier integrity (e.g., arXiv IDs, DOI validation), verify peer-review status when required, and~prioritize sources with complete metadata and proper attribution.
  \end{itemize}
  
  \textbf{\hl{Regulatory Compliance:} %MDPI: RESPONSE: Please leave as is.
}
  For domains operating in regulated fields (medicine, finance, law), evaluation pipelines include additional compliance checks such~as:
  \begin{itemize}
    \item \textbf{\hl{Disclosure requirements:} %MDPI: RESPONSE: Please leave as is.
} Ensure funding sources and conflicts of interest are identified when available.
    \item \textbf{\hl{Evidence hierarchy:} %MDPI: RESPONSE: Please leave as is.
} Prioritize higher-quality evidence types (systematic reviews over individual studies, peer-reviewed over preprints).
    \item \textbf{\hl{Risk communication:} %MDPI: RESPONSE: Please leave as is.
} Flag content that requires specific disclaimers or warnings based on domain regulations.
  \end{itemize}
  
  
  \subsubsection{Article Match Against Article Analysis~Database}\label{article-match}
  For the sake of efficiency, the~system maintains a cache of previously processed articles. When an article has been analyzed in a previous session, the~system  reuses the existing summary and analysis rather than reprocessing it from scratch. %This caching mechanism significantly reduces processing time and computational costs for frequently referenced~articles.
  
  %The system does not maintain a traditional relational database; instead, it leverages saved state configurations and cached summaries. New articles that have not been processed before undergo full analysis, while previously analyzed articles can be retrieved from cache when their identifiers match. This approach balances freshness (ensuring new articles are fully processed) with efficiency (avoiding redundant work on known articles).
  
  \subsubsection{ Text~Parsing}\label{full-text-parse}
  
  \NerdEngine extracts and processes content in multiple formats. For~open-access articles, the~full text is retrieved, cleansed of formatting artifacts, and~structured into key sections (abstract, introduction, methods, results, discussion). When full text is not available, the~system works with abstracts and metadata, attempting to extract maximum value from available~information.
  
  Users can also upload documents directly (PDF formats), which are processed in the same way as retrieved articles. %These uploaded documents are parsed, their text extracted, and~integrated into the analysis pipeline alongside database-sourced content. 
  This capability allows users to include proprietary research, internal documents, or~papers not available through public databases in their~queries.
  
  \subsubsection{Article~Summarization}\label{SoC-analysis}
  
  The system distinguishes between different types of sources and applies type-appropriate analysis strategies based on the domain. For~example, in~medical domains, primary research studies are analyzed for methodology, sample size, and~specific findings, while review articles are evaluated for completeness and synthesis quality. In~technical domains, questions are distinguished from answers, with~accepted solutions prioritized over alternative approaches. In~news domains, breaking news articles are distinguished from analysis pieces, with~recency and source credibility factored into evaluation. In~academic domains, peer-reviewed publications are distinguished from preprints, with~publication venue and citation metrics considered. This type-aware processing ensures that the strength and nature of evidence are properly contextualized according to domain-specific~standards.
  
  For each article, the~system extracts and structures key metadata (title, authors, publication source, publication date, identifiers) and generates a query-tailored summary that highlights aspects most relevant to the user's question. The~goal of this targeted summarization is to preserve  the most pertinent information from each source  for the final synthesis, rather than generic summaries that may miss question-specific~nuances.
  
  \subsubsection{Safety-Conscious Output~Synthesis}\label{output-synthesis}
  The final answer is synthesized from the processed articles, with~the prompts prioritizing high-quality evidence and explicitly highlighting risks, limitations, and~areas of uncertainty. The~synthesis process tries to balance comprehensiveness with accuracy, ensuring that strong claims are supported by robust evidence, while weaker or contested findings are appropriately~qualified.
  
  %Before synthesis, the system intelligently selects which articles to include based on relevance scores and token budget constraints. Using statistical methods to identify the most informative content, the system ensures that the most valuable evidence fits within processing limits while maintaining answer quality. 
  
  The final output includes inline citations that map back to source articles, using identifiers (such as DOI) when available, with~fallback mechanisms to ensure all claims are properly attributed even when metadata is~incomplete.
  
  
  \subsubsection{Advanced~Features}
  The configuration system includes several advanced features that accelerate domain implementation and improve user~experience:
  \begin{itemize}
    \item \textbf{\hl{AI-Assisted Code Generation:} %MDPI: RESPONSE: Please leave as is.
} The code generator accepts API documentation, code snippets, or~natural language descriptions and automatically generates integration functions that match the system's required interfaces.
    \item \textbf{\hl{AI-Assisted Prompt Generation:} %MDPI: RESPONSE: Please leave as is.
} The prompt generator uses the current prompt as context to suggest improvements, helping domain experts refine their prompts more~efficiently.
    \item \textbf{\hl{Real-Time Validation:} %MDPI: RESPONSE: Please leave as is.
} The interface provides immediate feedback on configuration inputs: Color inputs validate format, file uploads check size and type constraints, and~API key inputs indicate required versus optional fields.
    \item \textbf{\hl{Adaptive Theming:} %MDPI: RESPONSE: Please leave as is.
} The system automatically detects whether the selected color scheme represents a light or dark theme and adapts interface elements accordingly.
    \item \textbf{\hl{Productivity Shortcuts:} %MDPI: RESPONSE: Please leave as is.
} Keyboard shortcuts enable rapid configuration workflows, allowing experienced users to save time when switching between configuration tabs.
  \end{itemize}
  
  
  
  
  
  % -----------------------------------------------------------------
  \section{Website Interface~Architecture}\label{sec:5}
  
  The website architecture consists of a FastAPI backend and a frontend built with HTML, CSS, and~JavaScript.
  The frontend listens to Server-Sent Events (SSEs) from the backend to display live progress and final responses with references.
  Figure~\ref{fig:website-architecture} shows the overall data flow. The~end-to-end \NerdEngine request pipeline---from query generation through article collection, organization, filtering, synthesis, and~citation mapping, with per-stage progress streamed to the frontend via SSE---is illustrated in Figure~\ref{fig:custom-nerd-workflow}. A~comprehensive visual walkthrough of the user-facing interface and configuration options is provided in Appendix~\ref{app:web_interface}.
  
    \noindent\textbf{\hl{Component Responsibilities:} %MDPI: RESPONSE: Please leave as is.
}
  \begin{itemize}
    \item \textbf{\hl{Frontend (HTML, CSS, JS):} %MDPI: RESPONSE: Please leave as is.
} Handles user input/output, streams live updates via SSE, and~supports exporting results as PDF. The~user-facing interface and configuration options are detailed in Appendix~\ref{app:web_interface}.
    \item \textbf{\hl{Backend (FastAPI):} %MDPI: RESPONSE: Please leave as is.
} Orchestrates the RAG pipeline, interacts with AI models, and~exposes REST and SSE endpoints.
    \item \textbf{\hl{Storage (JSON State/History):} %MDPI: RESPONSE: Please leave as is.
} Persists cached history, manages configuration bundles, and~enables quick retrieval of session data. Saved state management is illustrated in Appendix~\ref{app:web_interface_saved_states}.
  \end{itemize}
  
  
  
  The complete API endpoint reference, background task orchestration, front-end implementation details, and~endpoint-to-function mapping are provided in Appendix~\ref{sec:impl_from_main_added}.
  
  \begin{figure}[H]

  \begin{tikzpicture}[
      every node/.style={font=\small},
      block/.style={
          rectangle,
          draw=black!60,
          fill=black!5,
          text width=10em,
          text centered,
          rounded corners,
          minimum height=4em
      },
      line/.style={draw, -latex', thick},
      align=center,
      node distance=3.2cm
  ]
  
  % Nodes
  \node[block] (frontend) {Frontend \\ (HTML, CSS, JS)};
  \node[block, below of=frontend] (backend) {Backend \\ (FastAPI)};
  \node[block, below of=backend] (storage) {Storage \\ (JSON State / History)};
  
  % Arrows
  \path[line] (frontend) -- node[midway, right]{User Query} (backend);
  \path[line] (backend) -- node[midway, right]{Persist / Retrieve} (storage);
  \path[line] (backend.west) to[out=180, in=180, looseness=1.4]
      node[midway, left]{SSE Stream + Final Results} (frontend.west);
  
  \end{tikzpicture}
  \caption{System architecture illustrating data flow between frontend, backend, and storage.}
  \label{fig:website-architecture}
  \end{figure}
  


  


  
  
  \vspace{1pt}
  
  
  \section{Deploying Alternative Large Language~Models}
  \label{sec:deploy_alt_llm}
  
  \NerdEngine supports four LLM backends: \hl{\textbf{OpenAI},} %MDPI: RESPONSE: Please leave as is.
 \hl{\textbf{Gemini},} %MDPI: RESPONSE: Please leave as is.
 \hl{\textbf{Claude},} %MDPI: RESPONSE: Please leave as is.
 and~\hl{\textbf{Ollama}.} %MDPI: RESPONSE: Please leave as is.
 Switching providers requires only updating the \texttt{LLM} variable in \texttt{variables.env} and re-testing existing prompts, with~migration guidance provided in Appendix~\ref{app:llm_migration}.
  
  \subsection{Cloud Providers (OpenAI, Gemini, Claude)}
\textls[-15]{  For cloud-hosted backends, the~operator sets \texttt{LLM} to the desired provider name, supplies the corresponding API key (\texttt{OPENAI\_API\_KEY}, \texttt{GEMINI\_API\_KEY}, or~\texttt{ANTHROPIC\_API\_KEY}),} and~optionally verifies connectivity using the \textbf{\hl{Test} %MDPI: RESPONSE: Please leave as is.
} button in the Environment Configuration tab, which performs a lightweight API call and reports success or failure inline. All subsequent pipeline stages---validity gating, query expansion, relevance classification, and~response synthesis---proceed without further changes to prompts or retrieval~logic.
  
  \color{black}
  \subsection{Ollama: Zero-Cost Fully Local Inference}
  
  \NerdEngine supports \textbf{\hl{Ollama} %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: Done
} (\url{https://ollama.com} last accessed May 2026), an~open-source runtime that serves open-weight language models entirely on the operator's own hardware. This means the system can be run completely locally: %MDPI: RESPONSE: Please leave as is.
 No API key is required, there is no per-token billing, and~no user query or document content is transmitted to any external server. This makes \NerdEngine viable in privacy-sensitive, offline, or~resource-constrained deployments where subscription-based cloud inference is not~feasible.
  
  %For example, models such as \textbf{LLaMA} (Large Language Model Meta AI) developed by Meta are supported via Ollama (\url{https://ai.meta.com/llama/}).
  
  For example, models such as LLaMA
 (Large Language Model Meta AI) developed by Meta are supported via Ollama.
  \subsubsection{Why Local Inference Matters}
  Cloud LLM providers impose per-token costs that can accumulate rapidly for high-volume or long-context research queries. They also require data to leave the operator's infrastructure, which may conflict with institutional data-governance policies. Ollama eliminates both constraints: inference executes on the local CPU or GPU, and~the operator retains full custody of all inputs and outputs. On~Apple Silicon hardware (M1 and later) and modern x86 workstations, 7--8 billion parameter models run at conversational speeds without specialist~accelerators.
  
    \begin{figure}[H]

    \includegraphics[width=0.8\textwidth]{figures/custom_nerd_workflow.png}
    \caption{\highlighting{\NerdEngine} %MDPI: Please check if overlap contents affect reading. 2. Please cite the Figure in the text and ensure that the first citation of each Figure appears in numerical order. RESPONSE: (1) Confirmed --- the overlapping elements in the workflow diagram are acceptable and do not affect readability; each stage label and arrow remains legible at print scale (source image is 2342 x 3840 px at 300 DPI). (2) Resolved --- Figure~\ref{fig:custom-nerd-workflow} is now first cited in the main text at the end of Section~\ref{sec:5} (Website Interface Architecture), immediately after the citation of Figure~\ref{fig:website-architecture}, preserving numerical order (Fig.~1 cited in Section~4.1, Fig.~2 and Fig.~3 cited in Section~5).
% Author response: Added an explicit first citation of Figure~\ref{fig:custom-nerd-workflow} at line 1194 in Section~\ref{sec:5}, in the same paragraph that first cites Figure~\ref{fig:website-architecture}. Numerical citation order (Fig.~1 -> Fig.~2 -> Fig.~3) is now preserved. Overlapping content confirmed as acceptable for print.
 workflow: User request triggers the multi-stage pipeline (query generation, article collection, organization, filtering, synthesis, citation mapping) with stages communicated via SSE to the~frontend.}
    \label{fig:custom-nerd-workflow}
  \end{figure}
  
  \subsubsection{Pre-Flight Status Check}
  When the Ollama tab is opened in the web interface, the~system immediately queries the local Ollama daemon (\texttt{GET/api/tags}) and displays three live status indicators: whether Ollama is installed, whether the server is running on port~11434, and~whether the selected model has already been downloaded. This pre-check prevents redundant setup steps and gives the operator immediate situational awareness before triggering any~installation.
  
  \color{black}
  
  \subsubsection{Model Selection and the Model Guide}
  The operator selects a model from a categorised dropdown (Recommended, Small, High Quality, Coding, Cloud) or enters a custom model name directly. A~built-in \emph{\hl{Model Guide} %MDPI: RESPONSE: Please leave as is.
} pop-up assists in this choice: it presents a filterable table of curated models with per-model parameter count, estimated disk footprint, minimum RAM requirement, relative inference speed, and~quality rating. The~guide offers two view modes toggled from the~header:
  \begin{itemize}
    \item \textbf{\hl{Basic mode} %MDPI: RESPONSE: Please leave as is.
}---shows a plain-language compatibility summary (e.g., ``MacBook M1 or later~$\cdot$~Windows / Linux PC with 8\,GB RAM''), %MDPI: RESPONSE: Please leave as is.
 suitable for non-technical users selecting hardware-appropriate models.
    \item \textbf{\hl{Advanced mode} %MDPI: RESPONSE: Please leave as is.
}---reveals the full technical breakdown: parameter count, disk size, minimum RAM, speed badge, and~quality badge.
  \end{itemize}
  
  If models are already installed on the local machine, they are detected automatically via the \texttt{/ollama\_status} endpoint when the guide opens, and~an Installed %MDPI: RESPONSE: Please leave as is.
 chip is displayed next to the relevant rows. Models found locally but absent from the curated list appear in a dedicated Installed on your machine %MDPI: RESPONSE: Please leave as is.
 section at the top of the table, and~are simultaneously added to the model~dropdown.
  
  \subsubsection{Automated Setup with Multi-Method Installation Fallback}
  When the configuration is saved, the~backend \texttt{/ollama\_setup} endpoint orchestrates four sequential steps, streaming real-time progress to the browser via Server-Sent Events so the operator can observe each stage without reloading the page:
  
  \begin{enumerate}
    \item \textbf{\hl{Installation (with fallback chain).} %MDPI: RESPONSE: Please leave as is.
} The system checks whether Ollama is present on the host. If~not, it attempts installation using a prioritised sequence of methods tailored to the following detected operating~system:
      \begin{itemize}
        \item \textit{\hl{macOS:} %MDPI: RESPONSE: Please leave as is.
} (1) official \texttt{curl} install script; (2) Homebrew (\texttt{brew install ollama}) if Homebrew is available; (3) direct binary download from the GitHub Releases page to \texttt{/usr/local/bin}.
        \item \textit{\hl{Linux:} %MDPI: RESPONSE: Please leave as is.
} (1) official \texttt{curl} install script, which also registers and starts a \texttt{systemd} service automatically; (2) architecture-aware binary download (\texttt{amd64} or \texttt{arm64}) to \texttt{\textasciitilde/.local/bin}.
        \item \textit{\hl{Windows:} %MDPI: RESPONSE: Please leave as is.
} \textls[-35]{(1) official PowerShell install \hl{script} %MDPI: Please check if ''https://ollama.com/install.ps1'' should be placed in \url tag. RESPONSE: No --- this URL appears only inside the shell command \texttt{irm https://ollama.com/install.ps1 | iex} (code), not as a standalone reference, so it should remain in \texttt{} rather than be wrapped in \url{}.
 (\texttt{irm https://ollama.com/install.ps1 | iex}); }(2) \texttt{winget install Ollama.Ollama} if the Windows Package Manager is available.
      \end{itemize}
      Each method is attempted in order; the chain halts at the first success. If~all methods fail, a~\texttt{fatal} event is streamed to the browser with platform-specific manual instructions.
    \item \textbf{\hl{Server startup.} %MDPI: RESPONSE: Please leave as is.
} The backend starts the \texttt{ollama serve} daemon as a background process and polls \texttt{/api/tags} for up to 15 s, waiting for readiness before proceeding.
    \item \textbf{\hl{Model download.} %MDPI: RESPONSE: Please leave as is.
} \texttt{ollama pull <model>} is executed and its output is streamed line-by-line to the browser, giving the operator live download progress.
    \item \textbf{\hl{Verification.} %MDPI: RESPONSE: Please leave as is.
} The system confirms that the selected model appears in \texttt{/api/tags} and emits a \texttt{complete} event. If~the model is not immediately visible (rare on first install), a~warning message prompts a backend restart.
  \end{enumerate}
  
  \subsubsection{Inference Routing}
  At query time, the~existing OpenAI Python SDK is reused with its \texttt{base\_url} \hl{redirected to} %MDPI: Please check if 'http://localhost:11434/v1'' should be placed in \url tag. RESPONSE: Done
 \url{http://localhost:11434/v1}. All prompts---validity gating, query expansion, relevance classification, and~final response synthesis---execute unchanged against the local model, with~no modification to the pipeline. A~\texttt{\_safe\_create()} wrapper intercepts \texttt{NotFoundError} (model not pulled) and \texttt{APIConnectionError} (server not running) and surfaces them as actionable user-facing messages rather than opaque 500~errors.
  
  \subsubsection{Hardware Requirements and Model Selection Guidance}
  Table~\ref{tab:ollama_models} summarises the supported models with their resource profiles. Lightweight models (\texttt{llama3.2}, \texttt{phi3:mini}, \texttt{llama3.2:1b}) run on any modern laptop with 4--8\,GB of RAM and are recommended for first-time deployments. Mid-range models (\texttt{llama3.1:8b}, \texttt{qwen3:8b}, \texttt{mistral}) require 8--16\,GB RAM and deliver substantially higher response quality, suitable for Apple Silicon M1~Pro and above or x86 workstations. A~cloud-routed model (\texttt{kimi-k2.5:cloud}) is also available for operators who prefer a hosted fallback without switching providers in the environment~configuration.
  
  \begin{table}[H]

  \caption{Ollama model profiles available in \NerdEngine. RAM figures indicate the minimum recommended system memory for comfortable~operation.}
  \label{tab:ollama_models}
  \small
  \renewcommand{\arraystretch}{1.25}
  \begin{tabularx}{\textwidth}{XrrrlX}
  \hline
  \textbf{Model} & \textbf{Params} & \textbf{Disk} & \textbf{Min RAM} & \textbf{Speed} & \textbf{Best Suited for} \\
  \hline
  \texttt{llama3.2} & 3.2B & $\sim$2\,GB & 4\,GB & Fast & General use, first~deployment \\
  \texttt{llama3.1:8b} & 8B & $\sim$4.7\,GB & 8\,GB & Medium & Better reasoning, 128 K~context \\
  \texttt{llama3.2:1b} & 1B & $\sim$1.3\,GB & 3\,GB & Fastest & Very low-RAM~devices \\
  \texttt{phi3:mini} & 3.8B & $\sim$2.3\,GB & 4\,GB & Fast & Compact, punches above~weight \\
  \texttt{qwen3:8b} & 8B & $\sim$5\,GB & 8\,GB & Medium & Best reasoning at 8B~class \\
  \texttt{mistral} & 7B & $\sim$4.1\,GB & 8\,GB & Medium & Versatile general~purpose \\
  \texttt{codellama:7b} & 7B & $\sim$3.8\,GB & 8\,GB & Medium & Code generation~tasks \\
  \texttt{kimi-k2.5:cloud} & --- & None & None & Cloud & Cloud-routed, no local~download \\
  \hline
  \end{tabularx}
  \normalsize
  \end{table}
  
  The full model catalogue with Basic/Advanced hardware compatibility notes is available interactively through the Model Guide in the web interface, and~a static reference is provided in Appendix~\ref{app:ollama_local}.
  
  When the number of retrieved articles exceeds ten, \NerdEngine partitions them into parallel batches of ten---empirically, the maximum that Gemini 1.5 Pro processes without hallucinations---and synthesizes a bulleted summary for each batch independently. The~batch summaries are then concatenated and deduplicated: section titles across summaries are compared using cosine similarity, and~sections with a similarity score above 0.7 are merged, with~their inline citations combined. A~final LLM call condenses each merged section, preserving all source attributions while eliminating redundancy. The~full synthesis and summarization prompts are provided in Appendix~\ref{generate_response} and Appendix~\ref{summarize_output}, respectively. For~example, for~the query, ``What are the effects of the keto diet?'', Table~\ref{tab:keto-output-comparison} shows the concatenated result alongside the deduplicated~output.
  
  
  
  
  \section{Example Use Case Applications of \NerdEngine}\label{subsec:app-specialisation}
  
  We demonstrate the flexibility of \NerdEngine by implementing four case studies:
  DietNerd, NewsNerd, CloudNerd, and~SpaceNerd. Visual examples of each variant's home page interface are shown in Appendix~\ref{app:web_interface_variants}. You can find the main prompts for these applications in Appendix~\ref{prompt-templates}. 
  
  \subsubsection{DietNerd (Medical/Nutrition Research)}
  DietNerd specializes in evidence-based medical and nutrition research, prioritizing peer-reviewed sources and human evidence. The~system collects explicit health parameters through structured questionnaires, ensuring all clinical context is captured before retrieval~begins.
  
  \subsubsection{NewsNerd (Current Events Research)}
  NewsNerd focuses on current events with an emphasis on speed and recency. The~minimal questionnaire approach prioritizes user convenience, while the evaluation pipeline tests source credibility and performs~de-duplication. 
  
  \subsubsection{CloudNerd (Cloud Computing Research)}
  CloudNerd makes use of community-driven technical content from Stack Exchange, using  query expansion to generate comprehensive search variations. The~evaluation pipeline prioritizes accepted answers and solutions with reproducible configurations, while synthesis emphasizes security considerations and cost~implications.
  
  \subsubsection{SpaceNerd (Astronomy/Space Research)}
  SpaceNerd aggregates content from academic sources (arXiv, NASA). %while ensuring identifier integrity and peer-reviewed credibility. 
  The synthesis approach highlights areas of uncertainty and ongoing debates, reflecting the exploratory nature of space~science.
  
  Table~\ref{tab:domain_tradeoffs} summarizes the design goals and trade-offs of these four~applications. 
  
    The goals in Table~\ref{tab:domain_tradeoffs} illustrate the usefulness of  a  tool that allows a design team to decide in application-specific tradeoffs, e.g.,~between precision and understandability. The~configuration-first approach presents a consistent user interface layer  to apply to these four applications  seamlessly and thus will generalize to new applications. % Further, ; plug-and-play expansion of APIs or prompt bundles enables rapid domain launches; and saved-state snapshots guarantee deterministic restoration across~deployments.
  
  \begin{table}[H]
 
  \caption{Domain-Specific Design~Trade-offs.}
  \label{tab:domain_tradeoffs}
  \small
  \renewcommand{\arraystretch}{1.3}
  \begin{tabularx}{\textwidth}{p{3cm}XXXX}
  \hline
  \textbf{Trade-Off Dimension} & \textbf{DietNerd} & \textbf{NewsNerd} & \textbf{CloudNerd} & \textbf{SpaceNerd} \\
  \hline
  \textbf{\hl{Latency vs. Reliability} %MDPI: RESPONSE: Please leave as is.
} & 
  Tolerates longer retrievals in favor of rigor and evidence quality & 
  Must deliver within seconds; prioritizes speed & 
  Balances speed with technical depth; community validation provides quick quality signals & 
  Moderate latency acceptable for comprehensive academic~coverage \\
  \hline
  \textbf{\hl{Information Provenance vs. Understandability} %MDPI: RESPONSE: Please leave as is.
} & 
  Shows full references (AMA style); prioritizes provenance and rigor & 
  Hides references by default; prioritizes understandability & 
  Shows Stack Overflow links inline, favoring provenance & 
  Provides both provenance (arXiv, ADS handles) with plain-language~understandability \\
  \hline
  \textbf{\hl{Tone and Formality} %MDPI: RESPONSE: Please leave as is.
} & 
  Formal scientific language; accessible to lay readers & 
  Journalistic brevity; neutral tone & 
  Technical, snippet-heavy; preserves code precision & 
  Explanatory yet precise; highlights~uncertainties \\
%  \hline
%  \textbf{\hl{Scope of Filtering} %MDPI: RESPONSE: Please leave as is.
%} & 
%  Emphasises exclusion: animal studies, recipes, non-research queries & 
%  Emphasises deduplication: same events from multiple sources & 
%  Emphasises technical depth: security, best practices, reproducibility & 
%  Emphasises identifier consistency: arXiv IDs, DOI validation, metadata~completeness \\
  \hline
  \end{tabularx}
  \normalsize
  \end{table}
  
  
    \begin{table}[H]\ContinuedFloat  

  \caption{\emph{Cont.}}
  \label{tab:domain_tradeoffs}
  \small
  \renewcommand{\arraystretch}{1.3}
  \begin{tabularx}{\textwidth}{p{3cm}XXXX}
  \hline
  \textbf{Trade-Off Dimension} & \textbf{DietNerd} & \textbf{NewsNerd} & \textbf{CloudNerd} & \textbf{SpaceNerd} \\
  \hline
%  \textbf{\hl{Latency vs Reliability} %MDPI: RESPONSE: Please leave as is.
%} & 
%  Tolerates longer retrievals in favor of rigor and evidence quality & 
%  Must deliver within seconds; prioritizes speed & 
%  Balances speed with technical depth; community validation provides quick quality signals & 
%  Moderate latency acceptable for comprehensive academic~coverage \\
%  \hline
%  \textbf{\hl{Information Provenance vs Understandability} %MDPI: RESPONSE: Please leave as is.
%} & 
%  Shows full references (AMA style); prioritizes provenance and rigor & 
%  Hides references by default; prioritizes understandability & 
%  Shows Stack Overflow links inline favoring provenance & 
%  Provides both provenance (arXiv, ADS handles) with plain-language~understandability \\
%  \hline
%  \textbf{\hl{Tone and Formality} %MDPI: RESPONSE: Please leave as is.
%} & 
%  Formal scientific language; accessible to lay readers & 
%  Journalistic brevity; neutral tone & 
%  Technical, snippet-heavy; preserves code precision & 
%  Explanatory yet precise; highlights~uncertainties \\
%  \hline
  \textbf{\hl{Scope of Filtering} %MDPI: RESPONSE: Please leave as is.
} & 
  Emphasises exclusion: animal studies, recipes, non-research queries & 
  Emphasises deduplication: same events from multiple sources & 
  Emphasises technical depth: security, best practices, reproducibility & 
  Emphasises identifier consistency: arXiv IDs, DOI validation, metadata~completeness \\
  \hline
  \end{tabularx}
  \normalsize
  \end{table}
  

  
  The full installation guide and a step-by-step CloudNerd customization walkthrough are provided in Appendix~\ref{app:customnerd} with an overview in Section~\ref{sec:System_in_action}.
  
  
  
% CloudNerd evaluation section (split source for Overleaf).
% Include via \input{7-cloudnerd-evaluation.tex} from main.tex, or keep in sync with
% customnerdnew.tex (Section~\ref{sec:NerdEngine_evaluation}).

\section{CloudNerd~Evaluation}
\label{sec:NerdEngine_evaluation}

We evaluate CloudNerd on cloud-related Stack Overflow questions using semantic textual similarity (STS) and RAGAS metrics~\cite{es2023ragas}. CloudNerd performs multi-plane tiered retrieval via \texttt{tiered\_collect\_articles()} (CloudNerd code profile plus User Flow \emph{\hl{Deep Search} %MDPI: RESPONSE: Please leave as is.
} toggle): planes P1--P5 in \texttt{helper\_functions.py} bind to Stack Overflow via \texttt{user\_search\_apis.py}, merge results across planes, and~pass \texttt{retrieval\_confidence} into synthesis (Appendix~\ref{app:cloudnerd_eval_pipeline}). CloudNerd aims to avoid hallucinations by requiring the synthesis to be evidence-constrained (Appendix~\ref{app:cloudnerd_eval_pipeline}). Benchmark construction and per-question scores appear in Appendices~\ref{appendix:benchmark_construction} and~\ref{60-questions}.

This section reports the full evaluation program: a 20-question training set used for pipeline tuning and component ablations, and~a disjoint 120-question test benchmark for three-way comparison with OpenDeepResearch and AutoGPT. %All aggregate numbers, paired tests, and~win rates below are drawn from the primary eval artifacts (\texttt{eval\_120\_v1.5.csv}, \texttt{dennis-three-way-primary-120.csv}, and~\texttt{comparison-cloudnerd-vs-baselines-primary-120.csv}) in the project repository (Appendix~\ref{app:eval_run_history}).

\subsection{Comparison~Metrics}


  
Semantic textual similarity (STS) has been widely studied as the task of quantifying the
degree of semantic equivalence between pairs of texts, typically framed as a continuous
similarity score rather than a binary judgment~\cite{Cer2017SemEvalSTS}. Recent approaches to
STS commonly rely on transformer-based sentence embedding models, which encode sentences into a
shared vector space and compute similarity using cosine distance~\cite{ReimersGurevych2019SentenceBERT}.
This embedding-based evaluation paradigm has become standard for comparing generated responses
against reference answers in natural language generation and question-answering~settings.
  
Adopting STS and RAGAS~\cite{es2023ragas}, we compare CloudNerd with OpenDeepResearch and AutoGPT on a 120-question benchmark, with~training set comparisons in Appendix~\ref{app:eval_run_history}. Alternative evaluation frameworks such as FActScore~\cite{min2023factscore} have emerged. FActScore provides atomic scoring of factual precision in long-form text, but~we prioritize RAGAS here because its criteria (answer relevancy, faithfulness to retrieved context, and~answer similarity) align more directly with our cloud troubleshooting use~case.
  
\subsubsection*{Statistical Significance Testing in Empirical Evaluation}
  
  Empirical comparisons between systems require statistical testing to assess the robustness of
  observed performance differences. Non-parametric testing provides a distribution-free method
  for estimating statistical significance by repeatedly resampling observed outcomes, making it
  well-suited for settings with limited sample sizes or unknown score distributions~\cite{shasha2010statistics}. Such significance tests are commonly paired with
  confidence interval estimation to quantify the magnitude of performance differences between
  competing systems. We apply this non-parametric methodology in our evaluations here. %Section~\ref{sec:NerdEngine_evaluation}.
 
%STS uses \texttt{all-mpnet-base-v2} cosine similarity between the generated answer and the community-validated accepted answer~\cite{ReimersGurevych2019SentenceBERT,Cer2017SemEvalSTS}. RAGAS reports faithfulness (claims supported by retrieved contexts), answer similarity (semantic match to best answer), and answer relevancy (whether the answer addresses the question). We also report the mean per-question RAGAS geometric mean $\sqrt[3]{\text{faith}\times\text{sim}\times\text{rel}}$ (computed only when all three RAGAS scores are $>0$). RAGAS faithfulness complements STS for expertise domains. 


\subsection{Experimental~Setup}
\label{sec:eval_setup}


Table~\ref{tab:eval-systems-120} summarizes the three systems evaluated on the primary 120-question benchmark. OpenDeepResearch and CloudNerd share the same curated Stack Overflow JSONL corpus and retrieval temporal cutoff; AutoGPT answers the same question IDs via general web browsing without that corpus or the Stack Overflow~API.

\begin{table}[H]

\caption{Systems compared on the 120-question primary benchmark ($n=120$ per system).}
\label{tab:eval-systems-120}
\small
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{2.8cm} >{\raggedright\arraybackslash}X >{\raggedright\arraybackslash}X@{}}
\toprule
\textbf{System} & \textbf{Architecture} & \textbf{Evidence Access} \\
\midrule
CloudNerd & Saved-state pipeline: multi-plane tiered retrieval, citation recovery, evidence-constrained synthesis & Curated JSONL; Stack Overflow API; community ranking~signals \\
OpenDeepResearch & LangGraph Open Deep Research (supervisor--researcher--writer--critic)~\cite{chase2023langchain,langchain_odr_2025} & Same JSONL corpus and temporal cutoff; JSONL-only search with Post-ID citations (Appendix~\ref{OpenDeepResearchmods}) \\
AutoGPT & Autonomous tool-using agent~\cite{SignificantGravitas_AutoGPT_2023} & Same question IDs; web browsing without curated corpus or Stack Overflow~API \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\subsubsection{Datasets}
Table~\ref{tab:eval-datasets} lists the training and test splits. Each system's final configuration was tuned on the training set and evaluated once on the test benchmark. The~ground-truth answer for every question is the community-validated accepted answer (Appendix~\ref{appendix:benchmark_construction}).

\begin{table}[H]

\caption{Evaluation datasets (disjoint splits).}
\label{tab:eval-datasets}
\small
\renewcommand{\arraystretch}{1.15}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{3.4cm} >{\centering\arraybackslash}m{0.9cm} >{\raggedright\arraybackslash}X@{}}
\toprule
\textbf{Split} & \boldmath{$n$} & \textbf{Role} \\
\midrule
Training set & 20 & Pipeline tuning; component evolution \\%MDPI: table should be cited in order. tabel 14 should be cited after table 13. please revise. RESPONSE: Fixed. Removed the inline forward reference to Table~\ref{tab:devset-pipeline-evolution} (Table 14) from this table cell so the numerical-order rule is no longer violated. The "Pipeline tuning; component evolution" descriptor is sufficient here; readers reach the evolution table at its natural location in Section~\ref{sec:eval_components} where it is first-cited after Table~\ref{tab:cloudnerd-aggregates-120} (Table 13). No content loss.
Test benchmark (primary) & 120 & All reported three-way~comparisons \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\subsubsection{Benchmark Composition}
The primary benchmark (\texttt{stackoverflow\_with\_selection\_120\_v6.json}) contains 120 questions stratified from an initial pool of 720 Stack Overflow cloud-technology posts (Appendix~\ref{appendix:benchmark_construction}). The~first 60 questions emphasize implementation and how-to troubleshooting; the second 60 increase representation of causal and contrastive types. Table~\ref{tab:eval-question-types} summarizes the actionability mix. Questions were reformulated from Stack Overflow titles and bodies into natural-language queries a  user might ask, while the ground truth is the community-validated accepted answer for each~post.

\begin{table}[H]

\caption{Actionability categories in the 120-question test~set.}
\label{tab:eval-question-types}
\small
\renewcommand{\arraystretch}{1.15}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{4.2cm} >{\centering\arraybackslash}p{4cm} >{\centering\arraybackslash}p{4cm}@{}}
\toprule
\textbf{Category} & \boldmath{$n$} & \textbf{Share} \\
\midrule
Implementation/how-to & 44 & 36.7\% \\
Causal/explanatory & 17 & 14.2\% \\
Contrastive & 16 & 13.3\% \\
Other actionable & 43 & 35.8\% \\
\midrule
\textbf{\hl{Total} %MDPI: Please add an explanation for the use of bold in the table footer. If the bold is unnecessary, please remove it. The following highlights are the same. RESPONSE: Bold marks the summary/total row (and, in result tables, the best value per column); it is intentional and retained for emphasis.
} & \textbf{\hl{120} %MDPI: RESPONSE: Please leave as is.
} & \hl{\textbf{100\%}} %MDPI: RESPONSE: Please leave as is.
 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\subsubsection{Evaluation Protocol}
Each system's configuration was frozen after passing training set quality gates; the 120-question benchmark was evaluated once per system (to prevent tuning hyperparameters on the test set). CloudNerd's shipped configuration \hl{(\emph{cascade-v1.5},} %MDPI: RESPONSE: Please leave as is.
 May 2026) answered all 120 questions. %120/120 questions with %zero API failures, zero timeouts, and~
%no zero-retrieval cases. 
OpenDeepResearch and AutoGPT scores on the same 120 question IDs were merged from baseline CSV exports (Appendix~\ref{OpenDeepResearchmods}). Paired permutation tests (10{,}000 iterations, two-sided) and 95\% percentile bootstrap confidence intervals follow Appendix~\ref{statistical-methodology}. Internal repository run labels (\texttt{cascade-v1.5}, \texttt{combined-v1}, etc.) appear only in Appendix~\ref{tab:eval-run-history} for the purpose of~reproducibility.

\subsubsection{Pipeline Stages Under Test}
Every CloudNerd answer traverses the following stages: (1)~question validity gating, (2)~query expansion, (3)~multi-plane tiered retrieval (P1--P5; benchmark: P1 strict, P2 title-only, P4 extended keywords), (4)~article organization, (5)~LLM relevance filtering with optional TF-IDF citation recovery, (6)~evidence-constrained synthesis, (7)~citation matching, and~(8)~STS/RAGAS scoring. Supplemental retrieval \hl{(\emph{gap-fill})} %MDPI: RESPONSE: Please leave as is.
 runs when P1 coverage is thin or key question terms are missing from the retrieved~text.

\subsubsection{Temporal Constraint}
For each question, only Stack Overflow posts created before the ground-truth answer's posting date are visible to retrieval, matching the protocol in Appendix~\ref{appendix:benchmark_construction}. This prevents  answers that use the best~answer.


\subsubsection{Baselines}
On the 120-question benchmark test set, we compare CloudNerd against OpenDeepResearch and AutoGPT using the same question IDs, with~identical temporal cutoffs and the same accepted-answer ground truth. OpenDeepResearch uses the same curated Stack Overflow JSONL corpus as CloudNerd (Appendix~\ref{OpenDeepResearchmods}). AutoGPT answers the same question IDs via general web browsing without access to that corpus or the Stack Overflow API, representing autonomous agents that do not inherit a domain owner's curated retrieval configuration. Training set comparisons against both baselines appear in Appendix~\ref{app:eval_run_history}.

\subsubsection{Baseline Score Provenance}
OpenDeepResearch test set-120 scores come from a Stack Overflow--adapted LangGraph Open Deep Research run aligned to \texttt{stackoverflow\_with\_selection\_120\_v6.json} question IDs (\texttt{baseline-opendeepresearch-primary-120.csv}). %AutoGPT primary-120 scores come from an autonomous-agent baseline on the same IDs (\texttt{baseline-autogpt-primary-120.csv}). A~legacy CSV previously mislabeled as ``Jake'' matched AutoGPT aggregates exactly (STS 0.578, faithfulness 0.096); three-way tables in this section use the corrected Open Deep Research and AutoGPT exports~only.

\subsubsection{CloudNerd Evaluation Settings}
\textls[-25]{CloudNerd loads the \texttt{CloudNerd} cascade profile (\texttt{RETRIEVAL\_NERD\_PROFILES}) with \texttt{cascade\_retrieval.visible} enabled and three key retrieval flags:} \texttt{CITATION\_RESCUE\_ENABLED =true}, \texttt{RETRIEVAL\_GAP\_FILL\_ENABLED=true}, and~\texttt{ENABLE\_QUERY\_FALLBACK=false}. Final synthesis uses \texttt{openai\_prompts\_combined\_v1\_5.py} (four evidence-constrained rules on a direct-answer-first template) with \texttt{FINAL\_RESPONSE\_TEMPERATURE=0.2} and no two-stage faithfulness pass (Appendix~\ref{app:cloudnerd_eval_prompts}). % Faithfulness-oriented two-pass and strict-grounding variants were tested on subsets but withdrawn after collapsing answer relevancy (Table~\ref{tab:rejected-faithfulness}).

\subsubsection{Statistical Testing}
Paired permutation tests (10{,}000 iterations) with 95\% bootstrap confidence intervals follow Ref.~\cite{shasha2010statistics}, with~the methodology discussed in detail in Appendix~\ref{statistical-methodology}.

\subsection{Main Results: 120-Question~Benchmark}
\label{sec:eval_main_120}

%CloudNerd processed all 120 test questions successfully: 120/120 scored, zero failures, zero zero-retrieval cases. 
Table~\ref{tab:three-way-aggregates-120} is \textls[-15]{the test set comparison. Table~\ref{tab:winner-second-120} summarizes winner-vs-second-best margins. Tables~\ref{tab:cloudnerd-vs-odr-120} and~\ref{tab:cloudnerd-vs-autogpt-120} give paired head-to-head tests. %Table~\ref{tab:cloudnerd-win-rates-120} reports per-question strict win rates; 
Table~\ref{tab:cloudnerd-aggregates-120} lists CloudNerd retrieval~diagnostics.}

\begin{table}[H]

\caption{Three-way aggregate means on the 120-question test set benchmark ($n=120$). Bold = best mean per metric~column.}
\label{tab:three-way-aggregates-120}
\small
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{2.5cm} *{4}{>{\centering\arraybackslash}p{2.5cm}}@{}}
\toprule
\textbf{System} & \textbf{STS} & \textbf{Faith.} & \textbf{Rel.} & \textbf{Sim.} \\
\midrule
CloudNerd & \textbf{0.695} & 0.559 & 0.693 & \textbf{0.866} \\
OpenDeepResearch & 0.671 & \textbf{0.862} & \textbf{0.848} & 0.857 \\
AutoGPT & 0.578 & 0.096 & 0.670 & 0.823 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\vspace{-6pt}
\begin{table}[H]

\caption{Winner vs.\ second-best on the 120-question benchmark (paired permutation test, 10{,}000~iterations, 95\% bootstrap confidence intervals).}
\label{tab:winner-second-120}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{3pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{1.85cm} >{\raggedright\arraybackslash}X >{\raggedright\arraybackslash}X >{\centering\arraybackslash}m{1.2cm} >{\centering\arraybackslash}m{1cm} >{\centering\arraybackslash}m{1.8cm} >{\centering\arraybackslash}m{0.8cm}@{}}
\toprule
\textbf{Metric} & \textbf{Winner} & \textbf{2nd} & \textbf{Margin} & \boldmath{$p$} & \textbf{95\% CI} & \textbf{Sig.?} \\
\midrule
STS & CloudNerd & OpenDeepResearch & +0.024 & 0.011 & [+0.006, +0.042] & Yes \\
Faithfulness & OpenDeepResearch & CloudNerd & +0.303 & $<$0.001 & [+0.240, +0.367] & Yes \\
Answer Relevancy & OpenDeepResearch & CloudNerd & +0.155 & $<$0.001 & [+0.083, +0.229] & Yes \\
Answer Similarity & CloudNerd & OpenDeepResearch & +0.008 & 0.002 & [+0.003, +0.013] & Yes \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\vspace{-6pt}
\begin{table}[H]

\caption{Non-parametric paired permutation test: RAG-based CloudNerd vs.\ RAG-based OpenDeepResearch on 120 matched questions (10{,}000 iterations, 95\% bootstrap confidence intervals).}
\label{tab:cloudnerd-vs-odr-120}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{3pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{2.0cm} >{\centering\arraybackslash}m{1.05cm} >{\centering\arraybackslash}X >{\centering\arraybackslash}m{1.5cm} >{\centering\arraybackslash}m{0.75cm} >{\centering\arraybackslash}m{2.05cm} >{\centering\arraybackslash}m{0.9cm}@{}}
\toprule
\textbf{Metric} & \textbf{CloudNerd} & \textbf{OpenDeepResearch} & \boldmath{$\Delta$ \textbf{Mean}} & \boldmath{$p$} & \textbf{95\% CI} & \textbf{Sig.?} \\
\midrule
STS & 0.695 & 0.671 & +0.024 & 0.011 & [+0.006, +0.042] & Yes \\
Faithfulness & 0.559 & 0.862 & $-$0.303 & $<$0.001 & [$-$0.368, $-$0.239] & Yes \\
Answer Similarity & 0.866 & 0.857 & +0.008 & 0.002 & [+0.003, +0.013] & Yes \\
Answer Relevancy & 0.693 & 0.848 & $-$0.155 & $<$0.001 & [$-$0.230, $-$0.084] & Yes \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}
\vspace{-6pt}
\begin{table}[H]

\caption{Non-parametric paired permutation test: RAG-based CloudNerd vs.\ Agentic AutoGPT on 120 matched questions (10{,}000 iterations, 95\% bootstrap confidence intervals).}
\label{tab:cloudnerd-vs-autogpt-120}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{3pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}X >{\centering\arraybackslash}m{2.0cm} >{\centering\arraybackslash}m{1.5cm} >{\centering\arraybackslash}m{1.5cm} >{\centering\arraybackslash}m{1.5cm} >{\centering\arraybackslash}m{2.05cm} >{\centering\arraybackslash}m{0.9cm}@{}}
\toprule
\textbf{Metric} & \textbf{CloudNerd} & \textbf{AutoGPT} & \boldmath{$\Delta$ \textbf{Mean}} & \boldmath{$p$-\textbf{Value}} & \textbf{95\% CI} & \textbf{Sig.?} \\
\midrule
STS & 0.695 & 0.578 & +0.117 & $<$0.001 & [+0.087, +0.149] & Yes \\
Faithfulness & 0.559 & 0.096 & +0.463 & $<$0.001 & [+0.392, +0.530] & Yes \\
Answer Similarity & 0.866 & 0.823 & +0.042 & $<$0.001 & [+0.034, +0.051] & Yes \\
Answer Relevancy & 0.693 & 0.670 & +0.023 & 0.592 & [$-$0.061, +0.106] & No \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\begin{comment}
\begin{table}[H]

\caption{Per-question outcomes on the 120-question benchmark (strict win / tie / loss rates; CloudNerd compared to baseline on each metric).}
\label{tab:cloudnerd-win-rates-120}
\small
\renewcommand{\arraystretch}{1.15}
\setlength{\tabcolsep}{3pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{2cm} *{6}{>{\centering\arraybackslash}p{1cm}}@{}}
\toprule
\textbf{Metric} & \multicolumn{3}{c|}{\textbf{vs.\ OpenDeepResearch}} & \multicolumn{3}{c}{\textbf{vs.\ AutoGPT}} \\
\cmidrule(lr){2-4}\cmidrule(lr){5-7}
& \textbf{Win} & \textbf{Tie} & \textbf{Loss} & \textbf{Win} & \textbf{Tie} & \textbf{Loss} \\
\midrule
STS & 64\% & 0\% & 36\% & 79\% & 0\% & 21\% \\
Faithfulness & 20\% & 3\% & 78\% & 83\% & 13\% & 4\% \\
Answer Similarity & 71\% & 0\% & 29\% & 84\% & 0\% & 16\% \\
Answer Relevancy & 39\% & 1\% & 60\% & 47\% & 8\% & 45\% \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}
\end{comment}

On the 120-question test benchmark, CloudNerd leads in STS (0.695 vs.\ 0.671 OpenDeepResearch, 0.578 AutoGPT) and answer similarity (0.866 vs.\ 0.857 OpenDeepResearch, 0.823 AutoGPT). OpenDeepResearch leads on faithfulness (0.862 vs.\ 0.559 CloudNerd) and answer relevancy (0.848 vs.\ 0.693 CloudNerd). All four winner-vs-second-best margins are statistically significant at \(p<0.05\) (Table~\ref{tab:winner-second-120}), and~AutoGPT ranks last on every aggregate metric (Table~\ref{tab:three-way-aggregates-120}).

This split result highlights the quality tradeoff between two RAG-based systems with different orchestration strategies: CloudNerd is stronger on semantic alignment (STS/similarity), while OpenDeepResearch is stronger on claim grounding and direct question targeting (faithfulness/relevancy). The~paired comparisons below quantify this tradeoff~metric-by-metric.

\begin{table}[H]

\caption{CloudNerd %MDPI: Figure and Table should be cited after where it is first mentioned. So we move it here. RESPONSE: Confirmed. The new placement of this table immediately after its first text citation is correct.
 aggregates metrics and retrieval diagnostics on the 120-question benchmark ($n=120$).}
\label{tab:cloudnerd-aggregates-120}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{5.0cm} >{\centering\arraybackslash}m{1.15cm} >{\raggedright\arraybackslash}X@{}}
\toprule
\textbf{Metric} & \textbf{Mean} & \textbf{Notes} \\
\midrule
Scored questions & 120/120 & All questions~answered \\
STS (all-mpnet-base-v2 cosine) & 0.695 & SD 0.152; vs.\ ground-truth~answer \\
RAGAS Faithfulness & 0.559 & Grounded in retrieved~contexts \\
RAGAS Answer Similarity & 0.866 & vs.\ ground-truth~answer \\
RAGAS Answer Relevancy & 0.693 & 26 questions with relevancy $= 0$ \\
RAGAS Geometric Mean & 0.791 & $n=83$ questions with faith., sim., rel.\ all $> 0$ \\
\midrule
Retrieval strictness tier: strict & 84 & test set retrieval match (70\%) \\
Retrieval strictness tier: title-only & 33 & Relaxed title match (28\%) \\
Retrieval strictness tier: extended keywords & 3 & Expanded keyword match (2\%) \\
Supplemental retrieval triggered & 36 & Gap-fill when strict tier is~thin \\
Citation recovery enabled & 120/120 scored & Dev-set ablation: scorable rate 6/20 $\rightarrow$ 20/20 (Table~\ref{tab:devset-pipeline-evolution}) \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}


\begin{comment}
\paragraph{CloudNerd vs.\ OpenDeepResearch.}
Paired tests on 120 matched question IDs show CloudNerd ahead on STS ($\Delta = +0.024$, $p = 0.011$, 95\% CI $[+0.006, +0.042]$) and answer similarity ($\Delta = +0.008$, $p = 0.002$). OpenDeepResearch leads on faithfulness ($\Delta = -0.303$, $p < 0.001$) and answer relevancy ($\Delta = -0.155$, $p < 0.001$). CloudNerd wins 64\% of STS comparisons and 71\% of similarity comparisons; it wins only 20\% of faithfulness and 39\% of relevancy comparisons (Table~\ref{tab:cloudnerd-win-rates-120}). This split pattern is consistent with the architectural contrast in Table~\ref{tab:nerdengine-vs-langgraph}: curated community retrieval and accepted-answer alignment favor STS and similarity, whereas OpenDeepResearch's writer--critic loop grounds claims more tightly in retrieved context on the shared corpus.
\end{comment}

\subsubsection{CloudNerd vs.\ AutoGPT.}
CloudNerd is significantly better on STS ($\Delta = +0.117$, $p < 0.001$), faithfulness (\mbox{$\Delta = +0.463$,} $p < 0.001$), and~answer similarity ($\Delta = +0.042$, $p < 0.001$). Answer relevancy shows a small positive mean difference ($\Delta = +0.023$) but the p-value is too high to declare the difference statistically significant. %Win rates vs.\ AutoGPT are 79\% (STS), 83\% (faithfulness), 84\% (similarity), and~47\% (relevancy). AutoGPT's aggregate faithfulness (0.096) reflects both weaker grounding on thin web retrieval and RAGAS sensitivity when retrieved contexts are~sparse.



\begin{comment}
\paragraph{RAGAS geometric mean, score variability, and~retrieval mix.}
CloudNerd's mean per-question RAGAS geometric mean is 0.791 over the 83 questions where faithfulness, similarity, and~relevancy are all $> 0$ (Table~\ref{tab:cloudnerd-aggregates-120}). STS shows meaningful spread (mean 0.695, SD 0.152, range 0.14--0.95), reflecting heterogeneous question difficulty across cloud platforms and error types. On~the primary benchmark, 84 questions used strict-tier retrieval, 33 used title-only fallback, 3 used extended-keyword fallback, and~supplemental retrieval triggered on 36 questions. Title-only and extended-keyword tiers correlate with lower relevancy on the development set (Table~\ref{tab:devset-tier-autogpt}), suggesting that fallback tiers remain a quality bottleneck even when coverage is~complete.

\paragraph{Metric discordance.}
Twenty-six test questions receive RAGAS answer relevancy $= 0$ despite non-empty answers; on that subset, mean STS is still 0.655 and 12 questions exceed STS 0.7. These cases illustrate why semantic similarity alone is insufficient: an answer can overlap lexically with the accepted solution while RAGAS penalizes answer structure or perceived off-target phrasing. Conversely, CloudNerd outscores OpenDeepResearch on faithfulness on 24 of 120 individual questions (Table~\ref{tab:cloudnerd-win-rates-120}) even though OpenDeepResearch wins the aggregate faithfulness comparison---another instance where paired per-question outcomes diverge from mean effects.
\end{comment}





\subsection{Limitations}
\label{sec:eval_limitations}

\begin{itemize}
  \item \textbf{\hl{RAGAS relevancy zeros:} %MDPI: RESPONSE: Please leave as is.
} 26 of 120 test questions have relevancy $= 0$ despite non-empty answers, usually due to answer structure or thin retrieval rather than empty contexts.
  \item \textbf{\hl{Faithfulness vs. OpenDeepResearch:} %MDPI: RESPONSE: Please leave as is.
} On the 120-question benchmark, OpenDeepResearch scores higher on faithfulness ($\Delta = -0.303$, $p < 0.001$; Table~\ref{tab:cloudnerd-vs-odr-120}). We will see that concatenating the answers increases faithfulness even further. % closing this gap without sacrificing relevancy remains open. %The training set shows a similar pattern at smaller $n$ (Table~\ref{tab:devset-vs-baselines}).
  \item \textbf{\hl{Community-based ground-truth answer:} %MDPI: RESPONSE: Please leave as is.
} We use accepted Stack Overflow answers as ground truth; alternative community-validated answers may differ in wording or~substance.
  %\item \textbf{Latency and cost:} Per-question wall-clock time and token totals were not logged for the primary 120-question runs. Design-level comparison appears in Section~\ref{sec:NerdEngine_evaluation} (Discussion); instrumented cross-system measurements are planned.
 % \item \textbf{Component telemetry:} Table~\ref{tab:cloudnerd-aggregates-120} reports retrieval-tier counts (84 strict, 33 title-only, 3 extended-keyword) and supplemental retrieval on 36/120 questions; per-question citation-recovery counts were not exported in the primary eval artifact.
\end{itemize}

\textls[-25]{Details of benchmark construction, statistical methodology, the~full 120-question score table, and~the complete evaluation run history appear in Appendices~\cref{appendix:benchmark_construction,statistical-methodology,60-questions,app:eval_run_history}.}

\color{black}
\subsection{Lessons from Tuning on the Training~Set}

\subsubsection{Component~Contributions}
\label{sec:eval_components}

Table~\ref{tab:devset-pipeline-evolution} summarizes how each pipeline mechanism changed scores on the 20-question training set  during CloudNerd refinement. Rows are sequential design steps;  each row isolates the effect of adding one mechanism while holding the benchmark split fixed ($n=20$). Appendix \cref{tab:eval-run-history,tab:devset-key-runs,tab:prompt-evolution} condense the full repository run log and prompt experiments documented in \texttt{CLOUDNERD\_EVALUATION\_HISTORY\_DETAILED.md}.%; rows marked \emph{exclude} or \emph{withdrawn} are audit-only and must not be cited as primary~evidence.


\begin{table}[H]

\caption{\hl{Training} %MDPI: Figure and Table should be cited after where it is first mentioned. So we move it here. RESPONSE: Confirmed
 set ($n=20$) pipeline evolution toward the evaluated CloudNerd system. Each row adds mechanisms on top of tiered retrieval (P1--P5). \emph{\hl{Scored} %MDPI: RESPONSE: Please leave as is.
} = questions with non-empty citations and RAGAS/STS~scores.}
\label{tab:devset-pipeline-evolution}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{3pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{2.4cm} >{\raggedright\arraybackslash}X >{\centering\arraybackslash}m{1.0cm} *{4}{>{\centering\arraybackslash}m{1.5cm}}@{}}
\toprule
\textbf{Milestone} & \textbf{Key Change} & \textbf{Scored} & \textbf{Faith} & \textbf{Rel} & \textbf{STS} & \textbf{Fallbacks} \\
\midrule
Tiered retrieval only & Tiered retrieval; strict relevance filter; no citation recovery & 6/20 & 0.627 & 0.447 & 0.587 & 11 \\
Citation recovery pipeline & + TF-IDF post-matcher recovery + softened relevance classifier & 20/20 & 0.455 & 0.486 & 0.677 & 5 \\
Direct-answer synthesis prompt & + obligating final-response template (direct answer first) & 19/20 & 0.500 & 0.701 & 0.668 & 0 \\
CloudNerd (evaluated) & + evidence-constrained answer rules + supplemental retrieval + $T=0.2$ & 20/20 & 0.571 & 0.803 & 0.664 & 0 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\paragraph{Legacy Baselines (Training Set)}
Strict single-pass retrieval with a hard relevance classifier correctly scored only 5 of the 20 training questions (faithfulness = 0.864 for those 5 questions, while~75\% of the questions remain unscorable). Relaxed query fallback increased coverage to 11 of the 20 training questions, but did not resolve the citation pipeline issue: posts were retrieved but subsequently discarded before synthesis. These issues motivated the development of a cascade retrieval and citation recovery approach.




\paragraph{Citation Recovery and Softer Relevance Classification}
With tiered retrieval alone and a strict relevance classifier, only 6 of the 20 training set questions were scorable despite retrieval succeeding on most questions---articles were dropped before synthesis, leaving empty citation objects. Enabling TF-IDF citation recovery and a softer relevance classifier that accepts Docker, Kubernetes, Terraform, and~LibreOffice deployment posts raised the scorable rate to 20/20. Fallback refusals dropped from 11 to 5 because more posts reached the final prompt, but~answer relevancy remained low (0.486) with the original final-response~wording.

\paragraph{Direct-Answer and Evidence-Constrained Synthesis Prompts}
Replacing the original final prompt with a direct-answer-first template increased RAGAS answer relevancy from 0.486 to 0.701 and eliminated all fallback refusals on the training set. CloudNerd adds four evidence-constrained answer rules on top of that template: (1) no configuration keys or commands absent from evidence; (2) direct answer in sentence 1 reusing question terms; (3) no synthesis across posts unless one of the posts states the conclusion; (4) gap statements last only. On~the training set, this step further improved faithfulness (0.571 vs. 0.500) and relevancy (0.803 vs. 0.701) while maintaining 20/20 scorable questions. Complete prompt listings appear in Appendices~\ref{app:cloudnerd_eval_prompts} and~\ref{app:cloudnerd_eval_pipeline}.

\paragraph{Supplemental Retrieval}
After tiered retrieval (P1--P5), supplemental Stack Overflow search runs when P1 results are thin ($\leq$2 articles) or when key question terms are missing from the retrieved text. On~the 120-question benchmark, supplemental retrieval triggered on 36/120 questions. Of~the 120 test questions, 84 used the strict retrieval tier, 33 used title-only retrieval, and~3 used extended-keyword retrieval. An~expanded supplemental-retrieval criterion (evaluated with an alternative prompt variant) failed the development-set quality gate (relevancy 0.803 $\rightarrow$ 0.753) and was not~deployed.

\subsubsection{Supplementary Development-Set~Analysis}
\label{sec:eval_devset_supp}


\textls[-25]{On the development set, CloudNerd outperforms OpenDeepResearch on STS (\mbox{$\Delta = +0.057$,}} $p = 0.021$) and answer similarity ($\Delta = +0.016$, $p = 0.016$). OpenDeepResearch remains stronger on faithfulness ($\Delta = -0.276$, not significant at $\alpha = 0.05$ on $n = 20$). CloudNerd exceeds AutoGPT on STS ($\Delta = +0.137$, $p < 0.001$), faithfulness ($\Delta = +0.485$, $p < 0.001$), and~similarity ($\Delta = +0.041$, $p < 0.001$); relevancy gains vs.\ AutoGPT are directionally positive but not significant at $n = 20$.

\begin{table}[H]

\caption{Development-set supplementary comparison: CloudNerd vs.\ OpenDeepResearch and AutoGPT ($n=20$). Paired permutation test; significance at $\alpha=0.05$.}
\label{tab:devset-vs-baselines}
\small
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{3.0cm} >{\centering\arraybackslash}p{6cm} >{\centering\arraybackslash}p{1.0cm} >{\centering\arraybackslash}p{1.35cm} >{\centering\arraybackslash}p{1.0cm}@{}}
\toprule
\textbf{Metric} & \boldmath{\textbf{vs.\ Open Deep Research} $\Delta$} & \boldmath{$p$} & \boldmath{\textbf{vs.}\ \textbf{AG} $\Delta$} & \boldmath{$p$} \\
\midrule
STS & +0.057 & 0.021 & +0.137 & $<$0.001 \\
Faithfulness & $-$0.276 & n.s. & +0.485 & $<$0.001 \\
Answer Similarity & +0.016 & 0.016 & +0.041 & $<$0.001 \\
Answer Relevancy & +0.012 & n.s. & +0.092 & n.s. \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}



\paragraph{Tier-Stratified Development-Set Analysis}
Tables~\ref{tab:devset-tier-autogpt} and~\ref{tab:devset-tier-faith-autogpt} stratify development-set means by the winning cascade strictness tier (direct-answer synthesis prompt configuration). CloudNerd leads AutoGPT on STS in every tier (+0.106 strict, +0.180 title-only, +0.133 extended keywords) and on faithfulness in every tier (+0.344, +0.231, +0.359 respectively). Relevancy beats AutoGPT on the strict tier (+0.051) but trails on title-only ($-$0.257) and extended-keyword ($-$0.150) tiers---harder questions where retrieval was forced to weaker fallback~levels.

\begin{table}[H]

\caption{Development-set tier-stratified means: CloudNerd vs.\ AutoGPT by winning retrieval strictness tier ($n=20$).}
\label{tab:devset-tier-autogpt}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.15}
\setlength{\tabcolsep}{3pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{1.5cm} >{\centering\arraybackslash}m{1cm} *{5}{>{\centering\arraybackslash}m{2cm}}@{}}
\toprule
\textbf{Tier} & \boldmath{$n$} & \textbf{STS (CloudNerd)} & \textbf{STS (AutoGPT)} & \boldmath{$\Delta$ \textbf{STS}} & \textbf{Rel (CloudNerd)} & \textbf{Rel (AutoGPT)} \\
\midrule
Strict & 11 & 0.618 & 0.512 & +0.106 & 0.795 & 0.744 \\
Title-only & 3 & 0.684 & 0.504 & +0.180 & 0.285 & 0.541 \\
Extended keywords & 6 & 0.698 & 0.565 & +0.133 & 0.586 & 0.736 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\vspace{-6pt}
\begin{table}[H]

\caption{Development-set faithfulness advantage of CloudNerd over AutoGPT by retrieval strictness tier ($n=20$).}
\label{tab:devset-tier-faith-autogpt}
\small
\renewcommand{\arraystretch}{1.15}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{3.0cm} >{\centering\arraybackslash}p{10cm}@{}}
\toprule
\textbf{Tier} & \boldmath{$\Delta$ \textbf{Faithfulness (\hl{CloudNerd} %MDPI: Please check if minus sign here is correct. RESPONSE: confirmed --- the sign is correct; this column reports the signed difference $\Delta$ = CloudNerd $-$ baseline.
 $-$ AutoGPT)}} \\
\midrule
Strict & +0.344 \\
Title-only & +0.231 \\
Extended keywords & +0.359 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}
\unskip

\subsubsection{Ablation and Hallucination-Control~Evidence}
\label{sec:ablation_hallucination}

We report three sources of evidence on whether hallucination-control mechanisms improve faithfulness without collapsing answer~quality.

\paragraph{Adding Evidence-Constrained Answer Rules}
On the training set, adding the four evidence-constrained rules improved faithfulness (+0.071) and relevancy (+0.102) relative to the direct-answer synthesis prompt without collapsing scorable rate (Table~\ref{tab:devset-pipeline-evolution}).

\paragraph{Withdrawn Strict-Grounding Designs}
Subset experiments with strict grounding synthesis prompts and two-stage verbatim synthesis raised faithfulness on the worst five extrapolation questions but drove relevancy to approximately 0.32 (Table~\ref{tab:rejected-faithfulness}). These designs were not deployed because they over-corrected the answer structure for RAGAS~relevancy.

\begin{table}[H]

\caption{Withdrawn faithfulness-oriented configurations on problem-question subsets (not deployed). Strict grounding raised faithfulness in difficult cases but collapsed answer~relevancy.}
\label{tab:rejected-faithfulness}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}X >{\centering\arraybackslash}m{0.65cm} *{3}{>{\centering\arraybackslash}m{0.95cm}} >{\raggedright\arraybackslash}m{2.5cm}@{}}
\toprule
\textbf{Configuration} & \boldmath{$n$} & \textbf{Faith} & \textbf{Rel} & \textbf{Sim} & \textbf{Outcome} \\
\midrule
Strict grounding synthesis prompt & 13 & 0.454 & 0.335 & 0.851 & Not~deployed \\
Hybrid retrieval with TF-IDF relevance ceiling & 13 & 0.503 & 0.472 & 0.794 & Not~deployed \\
Two-stage synthesis (verbatim fact binding) & 5 & 0.880 & 0.323 & 0.829 & Not~deployed \\
Two-stage synthesis (refined fact binding) & 5 & 0.876 & 0.322 & 0.832 & Not~deployed \\
Actionable-first prompt with expanded supplemental retrieval & 20 & 0.562 & 0.753 & 0.856 & Failed quality~gate \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\paragraph{Single-Knob Ablations (Set-B Training Set)}
Table~\ref{tab:setb-single-knob-ablations} reports dedicated runs on the 20-question training split ($n=20$), where each row changes exactly one mechanism relative to the evaluated CloudNerd configuration (\texttt{cascade-v1.5}). The~pattern is clear: citation rescue is the main coverage driver (16 of 20 scored and 4 zero-retrieval failures when disabled), supplemental gap fill is the main answer-relevancy driver (0.803 $\rightarrow$ 0.506 when disabled), and~legacy single-pass retrieval materially reduces robustness (13 of 20 scored, 7 zero-retrieval failures). These controlled ablations align with the sequential pipeline progression in Table~\ref{tab:devset-pipeline-evolution}.

\begin{table}[H]

\caption{Single-knob ablations on the 20-question training set ($n=20$). Each row changes one setting relative to evaluated CloudNerd (cascade-v1.5); all other settings match the~baseline.}
\label{tab:setb-single-knob-ablations}
\small
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{1.7cm} >{\raggedright\arraybackslash}X >{\centering\arraybackslash}m{1.05cm} >{\centering\arraybackslash}m{1.0cm} *{4}{>{\centering\arraybackslash}m{0.9cm}}@{}}
\toprule
\textbf{Run} & \textbf{Single Change vs.\ Baseline} & \textbf{\shortstack{Scored\\(n/20)}} & \textbf{\shortstack{Zero\\Ret.}} & \textbf{STS} & \textbf{Faith.} & \textbf{Rel.} & \textbf{Sim.} \\
\midrule
Baseline & None (evaluated config) & 20/20 & 0 & 0.664 & 0.571 & 0.803 & 0.861 \\
Abl.\ 1 & Disable citation rescue (\nolinkurl{CITATION_RESCUE_ENABLED=false}) & 16/20 & 4 & 0.653 & 0.636 & 0.660 & 0.858 \\
Abl.\ 2 & Disable supplemental gap fill (\nolinkurl{RETRIEVAL_GAP_FILL_ENABLED=false}) & 19/20 & 1 & 0.658 & 0.582 & 0.506 & 0.856 \\
Abl.\ 3 & Use old synthesis prompt (\nolinkurl{citation_relevancy_v1}) & 20/20 & 0 & 0.672 & 0.527 & 0.713 & 0.861 \\
Abl.\ 4 & Enable query fallback (\nolinkurl{ENABLE_QUERY_FALLBACK=true}) & 20/20 & 0 & 0.662 & 0.541 & 0.800 & 0.862 \\
Abl.\ 5 & Switch to legacy retrieval mode (\nolinkurl{RETRIEVAL_MODE=legacy}) & 13/20 & 7 & 0.654 & 0.507 & 0.680 & 0.856 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}
\unskip


\subsubsection{Telemetry on the 20-Question Training~Set}
\label{sec:training_telemetry}

To complement answer-quality metrics, we instrumented the 20-question training run (\emph{CloudNerd Deep Search ON,} %MDPI: RESPONSE: Please leave as is.
% Author response: Highlight removed. The CloudNerd Deep Search ON configuration label is the formal name of the tested configuration and is intentional.
} cascade P1--P5) with per-question telemetry exports (\texttt{benchmark\_results.csv/json}). The~run used API-key rotation across two 10-question batches and logs wall-clock runtime, input/output tokens, total tokens, and~estimated USD cost per~question. Per-question telemetry is summarized in Table~\ref{tab:setb-telemetry-summary}.

\begin{table}[H]

\caption{CloudNerd %MDPI: Please cite the table in the text and ensure that the first citation of each table appears in numerical order.
% Author response: Added explicit in-text citation Table~\ref{tab:setb-telemetry-summary} in the paragraph immediately preceding this table.
 Telemetry summary for the 20-question training set in cascade~mode.}
\label{tab:setb-telemetry-summary}
\small
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}X >{\centering\arraybackslash}p{3.2cm}@{}}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
Run size & 20~questions \\
\midrule
Total runtime & 7373.294~s \\
Average runtime per question & 368.665~s \\
\midrule
Total input tokens & 8{,}060{,}856 \\
Total output tokens & 474{,}234 \\
Total tokens & 8{,}535{,}090 \\
Average tokens per question (total) & 426{,}754.50 \\
Average tokens per question (input/output) & 403{,}042.80/23{,}711.70 \\
\midrule
Total estimated cost & \$94.835580 \\
Average estimated cost per question & \$4.741779 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

Runtime and cost are heterogeneous across questions: the median runtime is 358.524 s (min 321.377 s; max 507.017 s), and~per-question estimated costs range from USD 2.787760 to USD 9.807640. These telemetry values are reported for the 20-question training set~only.  

CloudNerd, OpenDeepResearch, and~AutoGPT use different control-flow patterns and tool invocation~counts. 

\subsubsection{Adjusted-Time Analysis Under Reduced 429~Backoff}
\label{sec:adjusted_time_429}

To quantify the runtime impact of retrieval throttling, we report an adjusted-time analysis using observed CloudNerd telemetry from the same 20-question run. Let %MDPI: Please ensure all variables/values in the equation appear in the same format in the text (normal/italic/bold/subscript/superscript). RESPONSE: All variables are typeset in math mode (italic with subscripts) both in the displayed equations and in any inline references. Variables \(t_{\min}\), \(t_{\text{avg}}\), \(n\), \(\Delta t\), \(\Delta T\), \(r\), \(t_{\text{adj}}\), and \(T_{\text{adj}}\) appear identically in the equations and the surrounding prose. Numeric values (321.377 s, 368.665 s, 47.288 s, 945.76 s, 7373.294 s) are kept as plain text consistent with MDPI house style. Format verified consistent.

\[
t_{\min}=321.377\ \text{s}, \quad t_{\text{avg}}=368.665\ \text{s}, \quad n=20.
\]

We %MDPI: Please check if the indented format has been maintained. RESPONSE: Confirmed. The displayed-equation indentation around this paragraph matches MDPI house style.
 define per-question overhead relative to the fastest observed question as
\[
\Delta t = t_{\text{avg}} - t_{\min} = 47.288\ \text{s},
\]
and total run-level overhead as
\[
\Delta T = n \cdot \Delta t = 945.76\ \text{s},
\]
which is 12.8\% of the observed total runtime (7373.294 s). This is a scenario estimate (not direct per-request attribution) of latency potentially recoverable if 429-driven backoff and effective serialization are~reduced.

For recovery fraction \(r \in [0,1]\), the~adjusted runtime is:
\[
t_{\text{adj}}(r) = t_{\text{avg}} - r\Delta t, \qquad
T_{\text{adj}}(r) = n \cdot t_{\text{adj}}(r).
\]


Interpretation: even partial recovery of throttling overhead is material. At~50\% recovery, expected savings are about 472.9 s over 20 questions (\(\sim\)7.9 min), with~throughput improving from 9.77 to 10.43 questions/hour. At~the floor proxy (100\% recovery), savings are 945.8 s (\(\sim\)15.8 min), and~throughput reaches 11.20 questions/hour. The full set of recovery scenarios is summarized in Table~\ref{tab:adjusted-time-scenarios}.

%We also instrumented AutoGPT on the same 20-question evaluation subset as a supplementary agentic baseline.
For AutoGPT, telemetry records on the 20-question evaluation subset shown in Table~\ref{tab:setb-telemetry-summary-autogpt} reflect end-to-end wall-clock runtime for each question and LLM usage reported by the agent execution loop, and~the five metrics are also~recorded.

\begin{table}[H]

\caption{Adjusted-time %MDPI: Please cite the table in the text and ensure that the first citation of each table appears in numerical order.
% Author response: Added explicit in-text citation Table~\ref{tab:adjusted-time-scenarios} in the paragraph immediately preceding this table.
 scenarios from reduced rate-limit overhead on the 20-question CloudNerd~run.}
\label{tab:adjusted-time-scenarios}
\small
\renewcommand{\arraystretch}{1.15}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{3.5cm} >{\centering\arraybackslash}m{3cm} >{\centering\arraybackslash}m{3cm} >{\centering\arraybackslash}m{3cm}@{}}
\toprule
\textbf{Recovered Overhead} & \textbf{Adjusted Avg Runtime (s/q)} & \textbf{Adjusted Total Runtime (20Q)} & \textbf{Throughput (q/hour)} \\
\midrule
0\% (observed baseline) & 368.665 & 7373.294 s & 9.77 \\
25\% of \(\Delta T\) & 356.843 & 7136.860 s & 10.09 \\
50\% of \(\Delta T\) & 345.021 & 6900.430 s & 10.43 \\
75\% of \(\Delta T\) & 333.199 & 6663.980 s & 10.81 \\
100\% of \(\Delta T\) (floor proxy) & 321.377 & 6427.540 s & 11.20 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}


\vspace{-6pt}
\begin{table}[H]

\caption{AutoGPT telemetry summary for the 20-question training~set.}
\label{tab:setb-telemetry-summary-autogpt}
\small
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}X >{\centering\arraybackslash}p{3.2cm}@{}}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
Run size & 20~questions \\
\midrule
Total runtime & 3208.006~s \\
Average runtime per question & 160.400~s \\
\midrule
Total input tokens & 462,936 \\
Total output tokens & 19,100 \\
Total tokens & 482,036 \\
Average tokens per question (total) & 24,101.80 \\
Average tokens per question (input/output) & 23,146.80/955.00 \\
\midrule
Total estimated cost & \$5.202360 \\
Average estimated cost per question & \$0.260118 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

Table~\ref{tab:setb-telemetry-summary-odr} shows the results for~OpenDeepResearch.  

\begin{table}[H]

\caption{OpenDeepResearch telemetry summary for the 20-question training~set.}
\label{tab:setb-telemetry-summary-odr}
\small
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}X >{\centering\arraybackslash}p{3.2cm}@{}}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
Run size & 20~questions \\
\midrule
Total runtime & 2656.627~s \\
Average runtime per question & 132.831~s \\
\midrule
Total input tokens & 1,178,817 \\
Total output tokens & 58,923 \\
Total tokens & 1,237,740 \\
Average tokens per question (total) & 61,887 \\
Average tokens per question (input/output) & 58,940.85/2946.15 \\
\midrule
Total estimated cost & \$13.56 \\
Average estimated cost per question & \$0.68 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\vspace{-6pt}

\begin{comment}
\begin{table}[H]
\centering
\caption{Withdrawn faithfulness-oriented configurations on problem-question subsets (not deployed). Strict grounding raised faithfulness on difficult cases but collapsed answer~relevancy.}
\label{tab:rejected-faithfulness}
\small
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}X >{\centering\arraybackslash}p{0.65cm} *{3}{>{\centering\arraybackslash}p{0.95cm}} >{\raggedright\arraybackslash}p{2.0cm}@{}}
\toprule
\textbf{Configuration} & \textbf{$n$} & \textbf{Faith} & \textbf{Rel} & \textbf{Sim} & \textbf{Outcome} \\
\midrule
Strict grounding synthesis prompt & 13 & 0.454 & 0.335 & 0.851 & Not~deployed \\
Hybrid retrieval with TF-IDF relevance ceiling & 13 & 0.503 & 0.472 & 0.794 & Not~deployed \\
Two-stage synthesis (verbatim fact binding) & 5 & 0.880 & 0.323 & 0.829 & Not~deployed \\
Two-stage synthesis (refined fact binding) & 5 & 0.876 & 0.322 & 0.832 & Not~deployed \\
Actionable-first prompt with expanded supplemental retrieval & 20 & 0.562 & 0.753 & 0.856 & Failed quality~gate \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\paragraph{Prompt experiments not deployed.}
Table~\ref{tab:rejected-faithfulness} summarizes faithfulness-oriented prompt variants tested on subsets during training. Strict grounding synthesis, hybrid TF-IDF relevance ceilings, and~two-stage verbatim synthesis raised faithfulness on difficult extrapolation questions but drove relevancy to approximately 0.32--0.35. An~expanded supplemental-retrieval prompt (combined-v1.6) failed the training set quality gate (relevancy 0.803 $\rightarrow$ 0.753) and was not run on the 120-question benchmark.
\end{comment}

\vspace{-6pt}

\color{black}
\subsection{Reproducibility and~Artifacts}
\label{sec:eval_reproducibility}

The evaluation artifacts are version-controlled in the Custom-Nerd repository %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”.
% Author response: Access date added to the URL on the next line in the standard MDPI format.
 (\url{https://github.com/Harsh23Kashyap/Custom-Nerd}, accessed on 25 June 2026):
\begin{itemize}
  \item CloudNerd 120-Q scores: \texttt{result/runs-120/cascade-v1.5/eval\_120\_v1.5.csv} (and matching \texttt{.json}, HTML report).
  \item Three-way aggregates and paired tests: \\\texttt{result/runs-120/OtherResearch/dennis-three-way-primary-120.csv},\\ \texttt{comparison-cloudnerd-vs-baselines-primary-120.csv}.
  \item Baseline exports: \\ \textls[-25]{\texttt{baseline-opendeepresearch-primary-120.csv}, \texttt{baseline-autogpt-primary-120.csv}.}
  \item Training set runs: \texttt{result/setB\_runs/cascade-v1.5/eval\_setB.csv} and related comparison HTML under \texttt{result/setB\_runs/comparison/}.
  \item Full run narrative: \texttt{result/CLOUDNERD\_EVALUATION\_HISTORY\_DETAILED.md}.
\end{itemize}

Benchmark questions: \texttt{stackoverflow\_with\_selection\_120\_v6.json} (Appendix~\ref{appendix:benchmark_construction}). Pin the repository commit hash when submitting to the journal or Overleaf data~archive.

\subsection{Discussion}

\subsubsection{Domain-Specific Optimization Trade-Offs}
%CloudNerd's curated Stack Overflow 
Retrieval and community-signal ranking improve alignment with ground-truth answers on STS and answer similarity compared with both OpenDeepResearch and AutoGPT on the 120-question benchmark. OpenDeepResearch scores higher on faithfulness and answer relevancy ($\Delta = -0.303$ and $-0.155$ vs.\ CloudNerd; Table~\ref{tab:cloudnerd-vs-odr-120}), indicating that its LangGraph writer--critic loop grounds claims more tightly in retrieved context when both systems share the same corpus. We treat this as an expected architectural trade-off (Table~\ref{tab:nerdengine-vs-langgraph}): bounded, configuration-first pipelines optimize scorable rate and accepted-answer alignment, whereas iterative agentic synthesis can prioritize claim-level grounding at higher token and latency cost. Closing the faithfulness gap without sacrificing relevancy remains open; strict two-stage grounding variants tested during development were not deployed because they over-corrected the answer structure for RAGAS~relevancy.

\subsubsection{Cost and Latency (Design-Level)}
We did not instrument wall-clock time or token usage for all 120-question runs in this submission, because~the CloudNerd, OpenDeepResearch, and~AutoGPT architectures are too different. Architecturally, CloudNerd uses a fixed sequence of bounded prompts with parallel relevance scoring, whereas OpenDeepResearch may execute additional graph hops when the critic requests revision. AutoGPT incurs open-web search latency without curated corpus reuse. Section~\ref{sec:eval_limitations} states this explicitly; a follow-up eval pass will log per-question latency and token totals for all three systems on the same hardware~profile.

\begin{comment}
\paragraph{Metric complementarity.}
The split win pattern across metrics (Tables~\ref{tab:winner-second-120} and~\ref{tab:cloudnerd-win-rates-120}) shows that semantic similarity alone cannot evaluate expertise-based systems; multi-dimensional RAGAS assessment and paired statistical testing are necessary. Faithfulness comparisons against OpenDeepResearch are not one-sided: CloudNerd wins on 20\% of questions, ties on 3\%, and~loses on 78\%, while against AutoGPT it wins 83\% with 13\% ties---consistent with curated retrieval grounding versus open-web search. AutoGPT's low aggregate faithfulness (0.096) reflects both weaker grounding and RAGAS sensitivity on thin retrieval contexts.
\end{comment}


\color{black}
\section{Combining CloudNerd with~OpenDeepResearch}
\label{sec:combining}
\label{combining}

Section~\ref{sec:NerdEngine_evaluation} shows that CloudNerd and OpenDeepResearch excel on different RAGAS dimensions on the same 120-question benchmark: CloudNerd leads on semantic textual similarity (STS) and answer similarity, whereas OpenDeepResearch leads on faithfulness and answer relevancy (Tables~\ref{tab:winner-second-120} and~\ref{tab:cloudnerd-vs-odr-120}).
After considering these complementary strengths, we ran a diagnostic experiment to test whether a naive hybrid---concatenating the two~systems' final answers---could improve aggregate scores without changing either~pipeline.

\subsection{\highlighting{Protocol} %MDPI: It should be heading 2 level, we revised all, please check and confirm. RESPONSE: confirm
}
For each of the 120 primary benchmark questions, we formed a \emph{\hl{concat} %MDPI: RESPONSE: Please leave as is.
} answer by appending the OpenDeepResearch synthesis after the CloudNerd synthesis, separated by a horizontal rule marker on its own line.
CloudNerd answers and retrieved contexts came from the canonical cascade-v1.5 run (\texttt{eval\_120\_v1.5.json}); OpenDeepResearch answers and passages came from the full LangGraph evaluation report (120 primary IDs).
We rescored all three conditions with the same metrics as Section~\ref{sec:NerdEngine_evaluation}: STS (\texttt{all-mpnet-base-v2} cosine vs.\ the accepted Stack Overflow answer) and RAGAS faithfulness, answer relevancy, and~answer similarity (GPT-4o-mini judge, ragas 0.1.21).
For RAGAS faithfulness on the concat condition, we supplied the \emph{\hl{union} %MDPI: RESPONSE: Please leave as is.
} of CloudNerd retrieved contexts and OpenDeepResearch passages---an optimistic upper bound because the judge sees evidence from both retrieval stacks.
Paired non-parametric permutation tests (10{,}000 iterations, same procedure as Appendix~\ref{statistical-methodology}) compare concat against each baseline on matched question IDs; $n=120$ except faithfulness on concat ($n=119$; one RAGAS timeout).

\subsection{Results}
Table~\ref{tab:aggregate-cn-odr-concat-primary-120} summarizes aggregate scores.
Concat improves STS relative to both baselines (0.722 vs.\ 0.695 CloudNerd and 0.671 OpenDeepResearch; both $p < 0.001$; Table~\ref{tab:comparison-cn-odr-concat-paired-primary-120}).
Merging the two answers moves generated text closer to the accepted community answer on average---likely because the combined text captures more of the ground-truth phrasing when the two systems emphasize different aspects of the~solution.

\begin{table}[H]

\caption{Aggregate metrics on the 120-question benchmark: CloudNerd, OpenDeepResearch, and~naive concat (CN answer + ODR answer).}
\label{tab:aggregate-cn-odr-concat-primary-120}
\small
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{3.2cm} >{\centering\arraybackslash}p{3cm} >{\centering\arraybackslash}p{4cm} >{\centering\arraybackslash}p{3cm}@{}}
\toprule
\textbf{Metric} & \textbf{CloudNerd} & \textbf{OpenDeepResearch} & \textbf{Concat} \\
\midrule
STS & 0.695 & 0.671 & \textbf{\hl{0.722} %MDPI: %MDPI: Please add an explanation for the use of bold in the table footer. If the bold is unnecessary, please remove it. The following highlights are the same. RESPONSE: Please leave as is
} \\
Faithfulness & 0.559 & 0.862 & \textbf{\hl{0.924}} %MDPI: RESPONSE: Please leave as is.
 \textsuperscript{a} \\
Answer Relevancy & 0.693 & \textbf{\hl{0.848} %MDPI: RESPONSE: Please leave as is.
} & 0.673 \\
Answer Similarity & \textbf{\hl{0.866} %MDPI: RESPONSE: Please leave as is.
} & 0.857 & 0.856 \\
\bottomrule
\end{tabularx}

\noindent{\footnotesize{\textsuperscript{a} Faithfulness on concat: $n=119$ (one timeout). Concat faithfulness uses union contexts from both systems.}}

\normalsize
\end{table}


\vspace{-9pt}
\begin{table}[H]

\caption{Paired permutation tests: naive concat vs.\ CloudNerd and vs.\ OpenDeepResearch on 120~matched questions (10{,}000 iterations).}
\label{tab:comparison-cn-odr-concat-paired-primary-120}
\small
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{3pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}p{3.7cm} >{\centering\arraybackslash}p{1.5cm} >{\centering\arraybackslash}p{1.5cm} >{\centering\arraybackslash}p{1.5cm} >{\centering\arraybackslash}p{1.5cm} >{\centering\arraybackslash}p{1.5cm} >{\centering\arraybackslash}p{1.5cm}@{}}
\toprule
\multirow{2}{*}{\textbf{Metric}\vspace{-6pt}} & \multicolumn{3}{c}{\textbf{Concat \boldmath{$-$} CloudNerd}} & \multicolumn{3}{c}{\textbf{Concat \boldmath{$-$} OpenDeepResearch}} \\
\cmidrule(l{0pt}r{0pt}){2-7}
& \boldmath{$\Delta$} & \boldmath{$p$} & \textbf{Sig.?} & \boldmath{$\Delta$} & \boldmath{$p$} & \textbf{Sig.?} \\
\midrule
STS & +0.027 & $<$0.001 & Yes & +0.051 & $<$0.001 & Yes \\
Faithfulness & +0.361 & $<$0.001 & Yes & +0.062 & 0.001 & Yes \\
Answer Relevancy & $-$0.021 & 0.599 & No & $-$0.176 & $<$0.001 & Yes \\
Answer Similarity & $-$0.009 & $<$0.001 & Yes & $-$0.001 & 0.546 & No \\
\bottomrule
\end{tabularx}
\noindent{\footnotesize{Faithfulness row: $n=119$ for concat comparisons. Negative $\Delta$ on answer similarity (concat vs.\ CloudNerd) indicates concat scores lower despite statistical significance.}}


\normalsize
\end{table}



On faithfulness, concat reaches 0.924---above CloudNerd (+0.361, $p < 0.001$) and OpenDeepResearch (+0.062, $p = 0.001$).
Longer answers judged against the union of both retrieval sets score as highly grounded.
This supports the hypothesis that the two~systems retrieve complementary evidence, but~the measurement is optimistic: faithfulness is computed against combined contexts, not against a single deployable retrieval~policy.

Answer relevancy tells the opposite story.
Concat scores 0.673---statistically indistinguishable from CloudNerd ($\Delta = -0.021$, $p = 0.599$) but significantly below OpenDeepResearch ($\Delta = -0.176$, $p < 0.001$).
Appending a second full synthesis often produces survey-style text; RAGAS penalizes diluted directness even when each system alone scored well on relevancy.
On several questions where CloudNerd had high relevancy ($>$0.9), concat relevancy dropped to 0.0 after append (e.g., question IDs 74470489, 65957504, 57764237).

Answer similarity remains high for all three conditions (0.856--0.866) and is essentially unchanged vs.\ OpenDeepResearch ($\Delta = -0.001$, $p = 0.546$); concat is slightly below CloudNerd ($\Delta = -0.009$, $p < 0.001$).

\subsection{Interpretation}
Naive concatenation is a useful diagnostic, not a deployable hybrid.
It confirms complementarity on STS and grounding (under union-context faithfulness) while exposing a sharp relevance cost relative to OpenDeepResearch's writer--critic synthesis.
A production hybrid would need \emph{selective} fusion---for example, using OpenDeepResearch to verify or refine a CloudNerd draft---rather than appending both answers in full.
Reproducibility scripts and score exports are under \texttt{result/runs-120/OtherResearch/cn-odr-concat-primary-120/} in the project repository; see Appendix~\ref{app:eval_run_history}.
\color{black}

  \section{Conclusions and Future~Work}
  \label{sec:conclusion}

\NerdEngine is a configuration-first framework for deploying expertise-driven retrieval-augmented generation systems without modifying core pipeline code. We know it is easy to get running, because~even biology students become operational with the system within three hours or~less.

Domain-specific behavior---prompts, retrieval logic, UI theming, and~deployment settings---is externalized into a saved-state configuration that domain experts can edit through a web interface. Four illustrative deployments (DietNerd, NewsNerd, CloudNerd, and~SpaceNerd) show that the same nine-stage pipeline adapts across medical, news, cloud-infrastructure, and~space-science use~cases.

On a 120-question Stack Overflow benchmark with community-validated ground truth, the~RAG-based CloudNerd and OpenDeepResearch showed complementary strengths. Both had better STS and RAGAS scores than the pure agentic approach of AutoGPT.
%completes all 120 questions with zero retrieval failures (Section~\ref{sec:NerdEngine_evaluation}). Three-way comparison against OpenDeepResearch and AutoGPT (Table~\ref{tab:cloudnerd-vs-odr-120}) shows that a configuration-first, curated-retrieval pipeline is \emph{competitive with} a strong LangGraph agentic baseline on the same corpus: CloudNerd leads significantly on semantic textual similarity and answer similarity, while OpenDeepResearch leads on faithfulness and answer relevancy. This is a metric-specific trade-off, not a failed evaluation---both systems excel on different dimensions of answer quality. Against AutoGPT on the same question IDs, CloudNerd significantly outperforms on three of four aggregate metrics and ranks ahead on every metric column (Table~\ref{tab:three-way-aggregates-120}), demonstrating 
This suggests that, for~now at least, curated community retrieval and evidence-constrained synthesis materially improve expertise question-answering over open-web autonomous search. %Multi-dimensional RAGAS assessment and paired permutation tests are therefore necessary to characterize system behavior~fairly.

\color{black}
As we show in Section~\ref{sec:combining}, even simple concatenation of CloudNerd and OpenDeepResearch syntheses improves STS and faithfulness (under union-context scoring) relative to either system alone, but~significantly reduces answer relevancy compared with OpenDeepResearch. Future work, therefore, includes integrating selective agentic verification---in the spirit of OpenDeepResearch's writer--critic loop---into the configuration-first pipeline to close the remaining faithfulness gap without sacrificing the saved-state, no-code deployment model or the answer similarity gains our direct-answer synthesis prompt achieved. Other future work includes reducing the query cost~substantially.

\vspace{6pt}
\authorcontributions{Conceptualization, H.K., S.W., and D.S.; methodology, H.K., S.W., and D.S.,; software, H.K., D.A., F.J., and J.D.; validation, H.K., D.A., F.J., E.K., and J.D.; formal analysis, H.K. and D.S.; investigation, H.K., D.A., F.J., S.W., E.K., E.H., and J.D.; resources, H.K. and D.S.; data curation, H.K. and D.A.; writing---original draft preparation, H.K., S.W., and D.S.; writing---review and editing, all authors; visualization, H.K. and S.W.; supervision, D.S.; project administration, D.S. All authors have read and agreed to the published version of the manuscript.%MDPI:For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used ``Conceptualization, X.X. and Y.Y.; methodology, X.X.; software, X.X.; validation, X.X., Y.Y. and Z.Z.; formal analysis, X.X.; investigation, X.X.; resources, X.X.; data curation, X.X.; writing---original draft preparation, X.X.; writing---review and editing, X.X.; visualization, X.X.; supervision, X.X.; project administration, X.X.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.'', please turn to the  \href{http://img.mdpi.org/data/contributor-role-instruction.pdf}{CRediT taxonomy} for the term explanation. Authorship must be limited to those who have contributed substantially to the work~reported.
% Author response: CRediT contributor-roles paragraph filled in per the supplied template. Funding acquisition role omitted because the research received no external funding (see \funding{} below).
}
 
\funding{This research received no external funding. The APC was funded by New York University.%MDPI:Please add: ``This research received no external funding'' or ``This research was funded by NAME OF FUNDER grant number XXX.'' and  and ``The APC was funded by XXX''. Check carefully that the details given are accurate and use the standard spelling of funding agency names at \url{https://search.crossref.org/funding}, any errors may affect your future funding.
% Author response: No external research grant funded this work. The article processing charge (APC) is being covered by New York University. To be confirmed by the corresponding author (D.S.) before submission; if a specific NYU department, institute, or grant code should be named (e.g., Courant Institute, a specific NIH/NSF grant), please update this line accordingly.
}

\dataavailability{The CustomNerd source code, evaluation artifacts, saved-state bundles, and the 120-question Stack Overflow benchmark are publicly available at\\ \url{https://github.com/Harsh23Kashyap/Custom-Nerd} (accessed on 25 June 2026). Per-question scores for CloudNerd, OpenDeepResearch, and AutoGPT on the 120-question benchmark are at\\ \texttt{result/runs-120/OtherResearch/dennis-three-way-primary-120.csv}. The benchmark question set is at \texttt{stackoverflow\_with\_selection\_120\_v6.json} (Appendix~\ref{appendix:benchmark_construction}). The CloudNerd--OpenDeepResearch concatenation diagnostic artifacts (Section~\ref{sec:combining}) are at\\ \texttt{result/runs-120/OtherResearch/cn-odr-concat-primary-120/}.%MDPI:In this section, please provide details regarding where data supporting reported results can be found, including links to publicly archived datasets analyzed or generated during the study. Please refer to suggested Data Availability Statements in section ``MDPI Research Data Policies'' at \url{https://www.mdpi.com/ethics}. You might choose to exclude this statement if the study did not report any data.
% Author response: Data Availability Statement provided pointing to the publicly archived GitHub repository, the per-question score CSV, the benchmark JSON, and the concatenation diagnostic artifacts. All paths are reproducible from a fresh clone of the repository.
}
\conflictsofinterest{H.K. is employed by Apple India Private Limited. The work was conducted independently of Apple's commercial interests; the company had no role in the design, execution, analysis, or reporting of the research. The other authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the~results.%MDPI:Declare conflicts of interest or state ``The authors declare no conflict of interest.'' Authors must identify and declare any personal circumstances or interest that may be perceived as inappropriately influencing the representation or interpretation of reported research results. Any role of the funders in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results must be declared in this section. If there is no role, please state ``The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the~results''.
% Author response: COI statement provided. Apple affiliation is disclosed for transparency; the work was conducted independently of any commercial interest.
}


\color{black}
%Naive strict-grounding variants tested during development collapsed answer relevancy and were not deployed (Table~\ref{tab:rejected-faithfulness}); an evaluated hybrid built on the current CloudNerd configuration remains open.


% References block deferred to the manual \begin{thebibliography} at the end of the appendix
% to consolidate per editorial feedback (Shasha, 29 June 2026: "There is one set at the very end
% of the appendix p. 149 and one before the appendix on p. 39. Please consolidate."). The
% canonical References list now appears once, after the appendix, at p.149 in the printed proof.

% Appendix material (installation, prompts, benchmark, stats)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Optional
\appendixtitles{yes}
\appendixstart


\appendix

\section{Custom Nerd Installation~Guide}\label{app:customnerd}

The main text already provides the step-by-step installation. This appendix only adds quick checks and reference~links.

\subsection{Prerequisites and Quick~Verification}


\textbf{\hl{Windows-specific:} %MDPI: RESPONSE: Please leave as is.
} When installing Python, ensure ``Add Python to PATH'' is~checked.

\paragraph{Verification procedure}
\begin{enumerate}
\item Open \texttt{Terminal} (macOS/Linux) or \texttt{PowerShell} (Windows).
\item Run \texttt{python3 --version} (\texttt{py -3 --version} on Windows).
\item Verify Python version is 3.11 or 3.12.
\end{enumerate}





\subsection{API Docs and~Front-End}

\begin{itemize}
    \item Backend \hl{API docs:} %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: Not applicable --- this is a local endpoint (http://localhost:8000/docs served by the running backend), not a public web resource, so no access date applies.
 \url{http://localhost:8000/docs} (served when the backend is running).
    \item Front-end preview: open \texttt{customnerd-website/index.html} in a browser; some features remain inactive until the backend is running.
\end{itemize}

\subsection{Configure Your API~Keys}

\begin{enumerate}
\item Open the file: \texttt{customnerd-backend/variables.env}
\item Set the \texttt{LLM} variable to your preferred provider: \texttt{OpenAI}, \texttt{Gemini}, \texttt{Claude}, or~\texttt{Ollama}.
\item If using a cloud provider, add the corresponding API~key:
\begin{itemize}
\item \texttt{OPENAI\_API\_KEY} – required when \texttt{LLM=OpenAI}
\item \texttt{GEMINI\_API\_KEY} – required when \texttt{LLM=Gemini}
\item \texttt{ANTHROPIC\_API\_KEY} – required when \texttt{LLM=Claude}
\end{itemize}
\item \textbf{\hl{Ollama (local, no API key required):} %MDPI: RESPONSE: Please leave as is.
} \textls[-25]{Set \texttt{LLM=Ollama} and optionally set \texttt{OLLAMA\_MODEL} (e.g., \texttt{llama3.2}) and \texttt{OLLAMA\_BASE\_URL} (default: \texttt{http://localhost:11434}). The~web interface will automatically install Ollama, download the selected model, and~start the server when you save the configuration.}
\item Optionally add data-source API~keys:
\begin{itemize}
\item \texttt{NCBI\_API\_KEY} – enhanced PubMed rate limits
\item \textls[-15]{\texttt{ELSEVIER\_API\_KEY}, \texttt{SPRINGER\_API\_KEY}, \texttt{WILEY\_API\_KEY}\hl{---optional} %MDPI: We revised it to emdash, please check and confirm. RESPONSE: confirmed.
 publisher data}
\end{itemize}
\item Save the file and restart the backend server.
\end{enumerate}

\begin{table}[H]

\small
\caption{System~requirements.}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{3.0cm} >{\raggedright\arraybackslash}m{5cm} >{\raggedright\arraybackslash}X@{}}
\toprule
\textbf{Requirement} & \textbf{Minimum Version} & \textbf{Purpose} \\
\midrule
Python & 3.11 or 3.12 & Executes the application~code \\
RAM & 4 GB (8 GB preferred) & Prevents excessive~swapping \\
Disk Space & 2 GB & Repository + virtual env~storage \\
Web Browser & Recent Chrome, Edge, Firefox & Accesses the~UI \\
Internet Connection & Stable & Downloads packages and~APIs \\
\bottomrule
\end{tabularx}

\end{table}

\subsection{Logs}

All installation logs are automatically saved in the \texttt{logs/} folder:
\begin{itemize}
\item \texttt{logs/presetup/} – Logs from \texttt{presetup.py} (Windows only)
\item \texttt{logs/setup/} – Logs from \texttt{setup.py}
\item \texttt{logs/run/} – Logs from \texttt{run.py}
\end{itemize}

Log files are named with the date and time (e.g., \texttt{setup\_24\_Dec\_2025\_8\_26\_pm.log}). If~you encounter errors, check these logs for detailed~information.

\subsection{Troubleshooting~Guide}

\textbf{\hl{Tip:} %MDPI: RESPONSE: Please leave as is.
} If you encounter errors during installation, try using ChatGPT or Gemini—they are really helpful for debugging installation issues!

\subsection{Estimated Installation~Time}

Most users finish installation in approximately 2 h; network speed and hardware may affect this figure. The~setup script downloads and installs many packages, which requires~patience.



\begin{table}[H]
\small
\caption{Common problems and~fixes.}
\renewcommand{\tabularxcolumn}[1]{m{#1}}
\setlength\tabcolsep{4pt}

\begin{tabularx}{\linewidth}{@{}>{\raggedright\arraybackslash}m{3.5cm}
                                  >{\raggedright\arraybackslash}m{3.5cm}
                                  X@{}}
\toprule
\textbf{Issue} & \textbf{Likely Cause} & \textbf{Recommended Action} \\
\midrule
\texttt{ModuleNotFoundError} &
Virtual environment not activated or missing dependencies &
Activate venv; run \texttt{setup.py} again or \texttt{pip install -r requirements.txt} \\[4pt]

\texttt{404 Not Found} &
Backend inactive or wrong port &
Check if server is running on port 8000; verify \texttt{run.py} executed successfully \\[4pt]

\texttt{OSError [Errno 98]} or \texttt{Address already in use} &
Port 8000 busy &
Free the port or modify \texttt{run.py} to use a different port \\[4pt]

Setup script fails &
Network issues or missing dependencies &
Check logs in \texttt{logs/setup/}; run \texttt{setup.py} again (it resumes from where it left off) \\[4pt]

WSL not working (Windows) &
WSL2 not properly installed &
Run \texttt{presetup.py} as Administrator; restart computer if~needed \\
\bottomrule
\end{tabularx}

\end{table}


\subsection{Conclusion}

Following these steps yields a fully operational
instance of Custom Nerd capable of retrieving scientific articles,
generating summaries, and~answering technical~queries.





\section{\NerdEngine Web Interface~Walkthrough}\label{app:web_interface}

This section provides a visual guide to using the \NerdEngine web interface, demonstrating how to navigate between different NerdEngine variants, manage saved states, and~configure the frontend~appearance.

\subsection{Home~Page}\label{app:web_interface_home}

Figure~\ref{fig:home-page} shows the main interface after the server starts. The~home page displays the \NerdEngine logo and a question input bar where users can submit queries. This is the default entry point for all NerdEngine~variants.

\begin{figure}[H]
  
  \fbox{\includegraphics[width=0.95\textwidth]{figures/home-page.png}}
  \caption{The \NerdEngine home page with question input~interface.}
  \label{fig:home-page}
\end{figure}
\unskip

\subsection{About~Page}\label{app:web_interface_about}

The \textbf{\hl{About} %MDPI: RESPONSE: Please leave as is.
} page provides information about the NerdEngine variant, including its purpose, data sources, and~any domain-specific disclaimers. Figure~\ref{fig:about-page} shows the About page interface, which can be customized through the Frontend Configuration~settings.

\begin{figure}[H]

  \fbox{\includegraphics[width=0.95\textwidth]{figures/about.png}}
  \caption{The About page displaying information about the active NerdEngine~variant.}
  \label{fig:about-page}
\end{figure}


\subsection{Preconfigured NerdEngine~Variants}\label{app:web_interface_variants}

\NerdEngine ships with five preconfigured NerdEngine variants, each tailored to a specific domain. \cref{fig:dietnerd-home,fig:spacenerd-home,fig:scinerd-home,fig:newsnerd-home,fig:cloudnerd-home} showcase the home pages for each variant, demonstrating how frontend customization creates distinct visual identities while maintaining consistent~functionality.

\begin{figure}[H]

  \fbox{\includegraphics[width=0.95\textwidth]{figures/dietnerd.png}}
  \caption{DietNerd home page: Evidence-based medical and nutrition research~assistant.}
  \label{fig:dietnerd-home}
\end{figure}

Each variant maintains the same core interface structure—question input bar, navigation menu, and~configuration access—while presenting domain-specific branding, colors, taglines, and~disclaimers configured through the Frontend Configuration~panel.

\subsection{Managing Saved~States}\label{app:web_interface_saved_states}

\NerdEngine ships with five preconfigured NerdEngine variants: \hl{\textbf{DietNerd},} \hl{\textbf{SpaceNerd},} %MDPI: RESPONSE: Please leave as is.
 \hl{\textbf{CloudNerd},} %MDPI: RESPONSE: Please leave as is.
 \hl{\textbf{SciNerd},} %MDPI: RESPONSE: Please leave as is.
 and~\hl{\textbf{NewsNerd}.} %MDPI: RESPONSE: Please leave as is.
 You can load any of these as starting templates, customize them, and~save your modifications as new states. This workflow enables rapid iteration: for example, after~building \hl{\textbf{LegalNerd},} %MDPI: RESPONSE: Please leave as is.
 you can save that state and immediately begin configuring \textbf{\hl{CalorieNerd} %MDPI: RESPONSE: Please leave as is.
} without losing your previous~work.




\begin{figure}[H]

  \fbox{\includegraphics[width=0.95\textwidth]{figures/spacenerd.png}}
  \caption{SpaceNerd home page: Astronomy and space science research~assistant.}
  \label{fig:spacenerd-home}
\end{figure}


\begin{figure}[H]

  \fbox{\includegraphics[trim=0 50 0 0, clip, width=0.95\textwidth]{figures/scinerd.png}}
  \caption{SciNerd home page: General scientific research~assistant.}
  \label{fig:scinerd-home}
\end{figure}

\begin{figure}[H]

  \fbox{\includegraphics[width=0.95\textwidth]{figures/newsnerd.png}}
  \caption{NewsNerd home page: Current events and news research~assistant.}
  \label{fig:newsnerd-home}
\end{figure}


\begin{figure}[H]

  \fbox{\includegraphics[width=0.85\textwidth]{figures/cloudnerd.png}}
  \caption{CloudNerd %MDPI: The contents of this figure are not legible. Please replace the image with one of a sufficiently high resolution (min. 1000 pixels width/height, or a resolution of 300 dpi or higher).
% Author response: Resolved. The figure has been updated to 300 DPI (was 144 DPI; pixel dimensions 2910 x 1538, well above the 1000-pixel floor). The LaTeX include width has also been reduced from 0.95\textwidth to 0.85\textwidth to raise effective print density. The same fix applies to the other home-page screenshots if needed.
 home page: Cloud computing and infrastructure troubleshooting~assistant.}
  \label{fig:cloudnerd-home}
\end{figure}

\subsubsection{Loading a Saved~State}\label{app:web_interface_load_state}

\textls[-35]{To load an existing state, navigate to the \textbf{\hl{Configurations} %MDPI: RESPONSE: Please leave as is.
} tab and select \highlighting{\textbf{Load Saved States}.}} %MDPI: RESPONSE: Please leave as is.
 Figure~\ref{fig:load-state} shows the interface displaying available saved states. Click on any state name to load it, then refresh the page and return to the \textbf{\hl{Home} %MDPI: RESPONSE: Please leave as is.
} tab to see that NerdEngine variant~active.

\begin{figure}[H]
 
  \fbox{\includegraphics[trim=150 0 150 0, clip, width=0.95\textwidth]{figures/configuration-tab1.png}}
  \caption{The Load Saved States interface showing available preconfigured NerdEngine~variants.}
  \label{fig:load-state}
\end{figure}
\unskip

\subsubsection{Saving a New~State}\label{app:web_interface_save_state}

After customizing prompts, search APIs, or~frontend settings, you can save your configuration as a new state. Figure~\ref{fig:save-state} illustrates the save interface. Enter a descriptive name (e.g., ``LegalNerd'' or ``CalorieNerd'') and click \highlighting{\textbf{Save State}.} %MDPI: RESPONSE: Please leave as is.
 The~system stores all configuration parameters—prompts, API settings, frontend customization, and~environment variables—as a reproducible bundle that can be loaded on any~machine.

\begin{figure}[H]

  \fbox{\includegraphics[trim=150 0 150 0, clip, width=0.95\textwidth]{figures/configuration-tab2.png}}
  \caption{\hl{The Save} %MDPI: Please check if incomplete contents in this figure affect reading, if so, please revise it. RESPONSE: The figure is an illustrative screenshot only; any incomplete content does not affect scientific understanding.
 State interface for persisting customized NerdEngine~configurations.}
  \label{fig:save-state}
\end{figure}


\subsubsection{Deleting~States}\label{app:web_interface_delete_state}

The interface also provides an option to delete saved states that are no longer needed. Figure~\ref{fig:delete-state} shows the delete functionality, allowing you to remove unwanted configurations and keep your workspace~organized.

\begin{figure}[H]

  \fbox{\includegraphics[trim=150 0 150 0, clip, width=0.95\textwidth]{figures/configuration-tab3.png}}
  \caption{The Delete State interface for removing unwanted saved~configurations.}
  \label{fig:delete-state}
\end{figure}
\unskip

\subsection{Frontend~Configuration}\label{app:web_interface_frontend}

The \textbf{\hl{Frontend Configuration} %MDPI: RESPONSE: Please leave as is.
} panel enables complete visual customization of your NerdEngine instance. Figures~\ref{fig:frontend-config1} and~\ref{fig:frontend-config2} show the comprehensive settings~available.

\begin{figure}[H]

  \fbox{\includegraphics[trim=300 0 300 0, clip, width=0.9\textwidth]{figures/configuration-tab4.png}}
  \caption{Frontend configuration settings (part 1): Site identity and branding~options.}
  \label{fig:frontend-config1}
\end{figure}

\begin{figure}[H]
 
  \fbox{\includegraphics[trim=300 0 300 0, clip, width=0.9\textwidth]{figures/configuration-tab5.png}}
  \caption{Frontend configuration settings (part 2): Visual styling and color~customization.}
  \label{fig:frontend-config2}
\end{figure}

The frontend configuration includes the following customizable elements:

\begin{itemize}
  \item \textbf{\hl{Site Name:} %MDPI: RESPONSE: Please leave as is.
} The display name shown in the navigation bar and page title (e.g., ``Diet Nerd'').
  \item \textbf{\hl{Site Logo:} %MDPI: RESPONSE: Please leave as is.
} Upload a custom logo image (max size: 5 MB) with optional alt text for accessibility.
  \item \textbf{\hl{Site Icon:} %MDPI: RESPONSE: Please leave as is.
} Choose an emoji or icon that appears in browser tabs and bookmarks (e.g., a~salad emoji for DietNerd).
  \item \textbf{\hl{Site Tagline:} %MDPI: RESPONSE: Please leave as is.
} A brief description that appears on the home page (e.g., ``We answer your diet and nutrition questions based on the strongest scientific evidence'').
  \item \textbf{\hl{Disclaimer:} %MDPI: RESPONSE: Please leave as is.
} Custom legal or medical disclaimer text displayed to users (e.g., educational purposes notice for medical domains).
  \item \textbf{\hl{Question Placeholder:} %MDPI: RESPONSE: Please leave as is.
} The placeholder text shown in the question input field (e.g., ``Enter your diet-related question here\dots.'').
  \item \textbf{\hl{Background Color:} %MDPI: RESPONSE: Please leave as is.
} Hex color code for the page background (e.g., \#EFF8FF).
  \item \textbf{\hl{Font Family:} %MDPI: RESPONSE: Please leave as is.
} Typography selection (e.g., Roboto).
  \item \textbf{\hl{Submit Button Color:} %MDPI: RESPONSE: Please leave as is.
} Hex color code for the primary action button (e.g., \#007bff).
\end{itemize}

The interface features smart color management that automatically handles text contrast and readability. You do not need to worry about color coordinates or potential issues with dark themes and dark text—the system intelligently adjusts text colors based on your background color selection to ensure optimal contrast and accessibility. Additionally, the~interface employs a modern glass morphism design style, providing a sleek, translucent aesthetic that enhances visual appeal while maintaining clarity and~usability.

After modifying any frontend settings, click the \textbf{\hl{Update} %MDPI: RESPONSE: Please leave as is.
} button to apply changes immediately, or~use \textbf{\hl{Reset} %MDPI: RESPONSE: Please leave as is.
} to revert to the default configuration. All frontend customizations are saved as part of the saved state bundle, ensuring visual consistency when loading states across different~deployments.

\subsection{Backend~Configuration}\label{app:web_interface_backend}

The \textbf{\hl{Backend Configuration} %MDPI: RESPONSE: Please leave as is.
} tab provides direct access to all prompt templates that control the NerdEngine pipeline behavior. Figures~\ref{fig:backend-config1} and~\ref{fig:backend-config2} show the comprehensive backend settings~interface.

The backend configuration interface displays all the prompt templates that control the NerdEngine pipeline, including:

\begin{itemize}
  \item \textbf{\hl{Question Validity Prompt:} %MDPI: RESPONSE: Please leave as is.
} Checks if user questions fit the domain and filters out off-topic queries.
  \item \textbf{\hl{General Query Prompt:} %MDPI: RESPONSE: Please leave as is.
} Expands user questions into effective search queries for each data source.
  \item \textbf{\hl{Query Contention Prompt:} %MDPI: RESPONSE: Please leave as is.
} Lists main debate points about a question and search queries for each.
  \item \textbf{\hl{Relevance Classifier Prompt:} %MDPI: RESPONSE: Please leave as is.
} Judges if retrieved content is relevant and safe.
  \item \textbf{\hl{Article Type Prompt:} %MDPI: RESPONSE: Please leave as is.
} Classifies articles as ``study'' or ``review'' for the right summary~format.
  \item \textbf{\hl{Abstract Extraction Prompt:} %MDPI: RESPONSE: Please leave as is.
} Pulls out structured details from research papers.
  \item \textbf{\hl{Review Summary Prompt:}} %MDPI: RESPONSE: Please leave as is.
 Summarizes key info from review papers.
  \item \textbf{\hl{Study Summary Prompt:} %MDPI: RESPONSE: Please leave as is.
} Gives structured details from individual studies.
  \item \textbf{\hl{Relevant Sections Prompt:} %MDPI: RESPONSE: Please leave as is.
} Maps article sections to standard categories.
  \item \textls[-25]{\textbf{\hl{Final Response Prompt:}} %MDPI: RESPONSE: Please leave as is.
 Synthesizes findings into a well-cited, domain-specific answer.}
  \item \textbf{\hl{Disclaimer Text:} %MDPI: RESPONSE: Please leave as is.
} Shows a custom disclaimer with each answer.
\end{itemize}

\begin{figure}[H]

  \fbox{\includegraphics[trim=300 0 300 0, clip, width=0.85\textwidth]{figures/configuration-tab6.png}}
  \caption{\hl{Backend} %MDPI: Please check if incomplete contents in this figure affect reading. RESPONSE: The figure is an illustrative screenshot only; incomplete content does not affect scientific understanding.
 configuration settings (part 1): Core prompt templates for the NerdEngine~pipeline.}
  \label{fig:backend-config1}
\end{figure}
\vspace{-6pt}

\begin{figure}[H]
 
  \fbox{\includegraphics[trim=300 0 300 0, clip, width=0.85\textwidth]{figures/configuration-tab7.png}}
  \caption{\textls[-25]{\hl{Backend} %MDPI: Please check if incomplete contents in this figure affect reading. RESPONSE: it is just an example, so is ok.
 configuration settings (part 2): Additional prompt templates and API~configurations.}}
  \label{fig:backend-config2}
\end{figure}




Each prompt template can be edited directly in the interface, allowing domain specialists to fine-tune the system's behavior without modifying code. After~making changes to any prompt templates, click the \textbf{\hl{Update Backend Configuration} %MDPI: RESPONSE: Please leave as is.
} button to apply changes, or~use \textbf{\hl{Reset} %MDPI: RESPONSE: Please leave as is.
} to restore the default prompts for the current saved state. All prompt modifications are persisted as part of the saved state, enabling reproducible domain configurations across~deployments.

\subsection{Environment~Configuration}\label{app:web_interface_environment}

\textls[-25]{The \textbf{\hl{Environment Configuration} %MDPI: RESPONSE: Please leave as is.
} tab manages all API keys required for your NerdEngine deployment. \cref{fig:env-config1,fig:env-config2,fig:env-config3} show the comprehensive environment settings~interface.}

\begin{figure}[H]

  \fbox{\includegraphics[clip, width=0.95\textwidth]{figures/configuration-tab8.png}}
  \caption{Environment configuration settings (part 1): Core API keys for LLM~providers.}
  \label{fig:env-config1}
\end{figure}


The environment configuration interface allows you to manage API keys for:

\begin{itemize}
  \item \textbf{\hl{LLM Providers:} %MDPI: RESPONSE: Please leave as is.
} The desired provider is selected via a four-way toggle (\hl{\textbf{OpenAI}/\textbf{Gemini}/\textbf{Claude}/ \textbf{Ollama}).} %MDPI: RESPONSE: Please leave as is.
 Cloud providers require the corresponding API key; each field has a built-in \textbf{\hl{Test} %MDPI: RESPONSE: Please leave as is.
} button that validates the key against the provider's API before saving. \textbf{\hl{Ollama} %MDPI: RESPONSE: Please leave as is.
} is the only option that requires no API key---it runs entirely on the local machine. When Ollama is selected, the~interface automatically installs Ollama (if absent), downloads the chosen model, and~starts the local server. Available Ollama models range from 1B-parameter lightweight models (e.g., \texttt{llama3.2:1b}, $\sim$1.3\,GB) to high-quality 14B models (e.g., \texttt{phi4}, $\sim$8.9\,GB); a built-in \emph{Model Guide} pop-up provides per-model disk, RAM, speed, and~quality information to help users~choose.
  
  \item \textbf{\hl{Data Source APIs:} %MDPI: RESPONSE: Please leave as is.
} Domain-specific API keys based on your project requirements, such~as:
  \begin{itemize}
    \item \textbf{\hl{PubMed/NCBI API Key:} %MDPI: RESPONSE: Please leave as is.
} Enhances rate limits for biomedical literature retrieval (optional but recommended for DietNerd).
    \item \textbf{\hl{News APIs:} %MDPI: RESPONSE: Please leave as is.
} Keys for news aggregation services (e.g., NewsAPI, GNews) for NewsNerd deployments.
    \item \textbf{\hl{Stack Overflow API Key:} %MDPI: RESPONSE: Please leave as is.
} Increases rate limits from 300 to 10,000 requests per day for CloudNerd deployments.
    \item \textbf{\hl{Publisher APIs:} %MDPI: RESPONSE: Please leave as is.
} Optional keys for Elsevier, Springer, and~Wiley for enhanced academic article access.
  \end{itemize}
  
  \item \textbf{\hl{Other Service Keys:} %MDPI: RESPONSE: Please leave as is.
} Additional API keys required by specific domain configurations or custom integrations.
\end{itemize}


After adding or modifying API keys, click the \textbf{\hl{Update Environment Configuration} %MDPI: RESPONSE: Please leave as is.
} button to save changes, or~use \textbf{\hl{Reset} %MDPI: RESPONSE: Please leave as is.
} to clear all API key fields. All environment variables are securely stored as part of the saved state, allowing you to deploy the same configuration across different environments. Note that API keys are never exposed in the frontend and are only used by the backend server for making authenticated requests to external~services.

\subsection{User Flow~Configuration}\label{app:web_interface_user_flow}

The \textbf{\hl{User Flow} %MDPI: RESPONSE: Please leave as is.
} tab provides toggle controls for enabling or disabling various search and interaction features. Each section includes a side-by-side inline SVG diagram (loaded from \texttt{user-flow-diagrams.js}) that compares toggle-off versus toggle-on behavior on the question page or in the retrieval pipeline. The~Deep Search diagram illustrates single-pass retrieval (toggle off) versus a simplified three-step Stack Overflow ladder---strict $\rightarrow$ title-only $\rightarrow$ extended-keyword---when the tiered cascade is active (toggle on). In~code, these steps correspond primarily to planes P1, P2, and~P4; plane~P3 adds a domain facet, and plane~P5 adds term gap-fill when quality gates do not short-circuit. \cref{fig:user-flow1,fig:user-flow2,fig:user-flow3} show screenshots of the configuration~interface.

\begin{figure}[H]
 
  \fbox{\includegraphics[trim=300 0 300 0, clip, width=0.9\textwidth]{figures/configuration-tab9.png}}
  \caption{Environment configuration settings (part 2): Data source API~keys.}
  \label{fig:env-config2}
\end{figure}


\begin{figure}[H]

  \fbox{\includegraphics[trim=300 0 300 0, clip, width=0.95\textwidth]{figures/configuration-tab10.png}}
  \caption{\textls[-25]{Environment configuration settings (part 3): Additional API keys and configuration~options.}}
  \label{fig:env-config3}
\end{figure}




\begin{figure}[H]

  \fbox{\includegraphics[trim=200 0 200 0, clip, width=0.95\textwidth]{figures/configuration-tab11.png}}
  \caption{User flow configuration (part 1): Normal Search, ID Specific Search, and~Local Document~Search.}
  \label{fig:user-flow1}
\end{figure}


\begin{figure}[H]

  \fbox{\includegraphics[trim=200 0 200 0, clip, width=0.95\textwidth]{figures/configuration-tab12.png}}
  \caption{User flow configuration (part 2): Query Cleaning and Deep Search tiered~retrieval.}
  \label{fig:user-flow2}
\end{figure}


\begin{figure}[H]

  \fbox{\includegraphics[trim=200 0 200 0, clip, width=0.95\textwidth]{figures/configuration-tab13.png}}
  \caption{\hl{User flow configuration} %MDPI: Please confirm whether the overlapping/incomplete content in this figure affects scientific understanding and if it does, please revise it. RESPONSE: It's just an illustration so ok.
 (part 3): Show References and Chat~History.}
  \label{fig:user-flow3}
\end{figure}

The user flow configuration includes the following toggleable features:

\begin{itemize}
  \item \textbf{\hl{Normal Search:} %MDPI: RESPONSE: Please leave as is.
} Enables standard article retrieval from configured data sources. This feature includes an AI code generator that automatically generates the search integration code. Provide the API endpoint and a basic description of how it works, and~the system generates and integrates the code for~you.

  \item \textbf{\hl{ID Specific Search:} %MDPI: RESPONSE: Please leave as is.
} Enables searching by specific identifiers (e.g., PubMed IDs, DOI numbers, arXiv IDs, Stack Overflow question IDs). Like Normal Search, this feature includes an AI code generator---provide the API and a basic description of its functionality, and~the system generates the integration code~automatically.

  \item \textbf{\hl{Local Document Search:} %MDPI: RESPONSE: Please leave as is.
} Enables processing of user-uploaded PDF documents. When enabled, users can upload PDF files that are then parsed and made searchable within the NerdEngine~pipeline.

  \item \textbf{\hl{Query Cleaning:} %MDPI: RESPONSE: Please leave as is.
} When enabled, LLM-generated query expansions pass through \texttt{clean\_query(query\_list, max\_queries=None)} in \texttt{clean\_query.py}, which parses JSON \texttt{expanded\_queries} (or plain nested lists) into a flat search list. Cleaned strings feed plane~P1 of tiered retrieval when Deep Search is active. An~AI code generator can regenerate this module from a bundled reference~implementation.

  \item \textbf{\hl{Deep Search:} %MDPI: RESPONSE: Please leave as is.
} \textls[-35]{User-facing label for multi-plane tiered cascade retrieval (\texttt{cascade\_retrieval. visible}} in \texttt{user\_env.js}). When on, \texttt{main.py} calls \texttt{tiered\_collect\_articles()} in \texttt{helper\_functions.py} instead of a single \texttt{collect\_articles()} pass; when off, legacy single-pass search runs regardless of profile. Cascade mode requires both this toggle \emph{and} a registered profile with \texttt{mode: "cascade"} in \texttt{RETRIEVAL\_NERD\_PROFILES} (CloudNerd, DietNerd). Planes run in order until quality gates pass or all enabled planes~complete:
  \begin{enumerate}[leftmargin=2em]
    \item \textbf{\hl{P1 (precision):} %MDPI: RESPONSE: Please leave as is.
} Expanded queries from \texttt{clean\_query()} (Stack Overflow: strict body/tag search).
    \item \textbf{\hl{P2 (relaxed):} %MDPI: RESPONSE: Please leave as is.
} \textls[-25]{Title and keyword variants generated from the resolved question title.}
    \item \textbf{\hl{P3 (facet):} %MDPI: RESPONSE: Please leave as is.
} Domain-scoped queries---CloudNerd applies a cloud lexicon; DietNerd appends the PubMed \texttt{[Humans]} facet.
    \item \textbf{\hl{P4 (broad):} %MDPI: RESPONSE: Please leave as is.
} Additional LLM-expanded and deterministic backup query batches.
    \item \textbf{\hl{P5 (gap-fill):} %MDPI: RESPONSE: Please leave as is.
} Targeted queries for key terms missing from merged results.
  \end{enumerate}
  
  \textls[-35]{The inline SVG contrasts single-pass retrieval (off) with the simplified strict$\rightarrow$title$\rightarrow$extended} ladder (on); see Appendix~\ref{app:tiered_retrieval} for the full P1--P5 plane list. Plane budgets, short-circuit rules, and~enabled plane lists are defined in code profiles, not in \texttt{variables.env} (Appendix~\ref{app:cloudnerd_eval_pipeline}).
  
  \item \textbf{\hl{Show References:} %MDPI: RESPONSE: Please leave as is.
} When enabled, displays inline citations and a references section with full bibliographic information for all sources used in the~response.
  
  \item \textbf{\hl{Chat History:} %MDPI: RESPONSE: Please leave as is.
} When enabled, saves all user queries and generated answers to a searchable history. The~system performs a similarity search on new queries to identify previously asked similar questions. Users can then choose to reuse the answer from a previous question or generate a new response, enabling efficient reuse of prior research while maintaining the option for fresh analysis.
\end{itemize}

All user flow settings can be toggled on or off independently, allowing you to customize the search and interaction experience for your specific domain requirements. Changes take effect immediately after toggling, and~all settings are saved as part of the saved state~configuration.

% -----------------------------------------------------------------
% Your existing table of meals follows; keep the \input line:
\input{tables/meals}
\section{Template Configuration~Models}\label{template-config}

We demonstrate the flexibility of \NerdEngine through four domain-specific instantiations—DietNerd, NewsNerd, CloudNerd, and~SpaceNerd—each realized via configurable metadata, prompt templates, and~execution pipelines. These case studies illustrate how \NerdEngine adapts retrieval sources, validation logic, synthesis behavior, and~citation strategies without modifying the core framework. The~main prompt templates for these applications are provided in Appendix~\ref{prompt-templates}.


\begin{lstlisting}[language=json, xleftmargin=0.5em, xrightmargin=0.5em, label={lst:domain-metadata}, caption={Listing A1: Domain-specific %MDPI: Please name it as "Listing A1". RESPONSE: Done. Caption renamed to "Listing A1: Domain-specific metadata used to configure Nerd variants" and a \label{lst:domain-metadata} added for cross-referencing.
 metadata used to configure Nerd variants.}]
{
  "DietNerd": {
    "domain": "academic_research",
    "inputs": [
      "free_text_query",
      "health_condition",
      "diet_constraints",
      "intervention_duration",
      "age_group",
      "citation_style"
    ],
    "data_sources": [
      "pubmed",
      "academic_db",
      "pdf_upload"
    ],
    "validation": [
      "peer_review_filter",
      "human_study_only",
      "exclude_case_reports"
    ],
    "bias_scoring": true,
    "citations": "full_academic"
  },
  "NewsNerd": {
    "domain": "current_events",
    "inputs": [
      "free_text_query",
      "topic_tag"
    ],
    "data_sources": [
      "news_api",
      "rss_feeds",
      "curated_sources"
    ],
    "validation": [
      "bias_and_duplicate_filter",
      "opinion_piece_exclusion"
    ],
    "bias_scoring": true,
    "citations": "suppressed"
  },
  "CloudNerd": {
    "domain": "cloud_computing",
    "inputs": [
      "free_text_query",
      "provider_tag",
      "deployment_type",
      "cost_sensitivity"
    ],
    "data_sources": [
      "stack_overflow_api",
      "aws_blog_rss",
      "azure_blog_rss",
      "cloudfest_rss",
      "vendor_blog_html_fetch"
    ],
    "validation": [
      "technical_relevance_filter",
      "security_flagging"
    ],
    "bias_scoring": false,
    "citations": "inline_links"
  },
  "SpaceNerd": {
    "domain": "astronomy_research",
    "inputs": [
      "free_text_query",
      "keyword_tag",
      "observation_date",
      "mission_reference"
    ],
    "data_sources": [
      "arxiv",
      "nasa_ads",
      "wikipedia",
      "nasa_images"
    ],
    "validation": [
      "identifier_consistency_check",
      "peer_review_priority"
    ],
    "bias_scoring": false,
    "citations": "hybrid"
  }
}
\end{lstlisting}

\subsection{Prompt Templates and Domain~Pipelines}\label{prompt-templates}

%\textbf{\textcolor{red}{TBD --- verify whether this section duplicates other prompt appendices; merge and eliminate redundancy if needed.}{\textcolor{blue}{--- Done}}}

Each Nerd variant combines a lightweight system prompt with a domain-specific retrieval, validation, and~synthesis~pipeline.

\textls[-15]{{This section summarizes the high-level prompt and retrieval strategy for each shipped Nerd variant. CloudNerd-specific evaluation prompts and pipeline details appear in \mbox{Appendices~\ref{app:cloudnerd_eval_prompts} and~\ref{app:cloudnerd_eval_pipeline};} the walkthrough in Section~\ref{sec:System_in_action} covers CloudNerd Advanced configuration.}}

\subsubsection{\highlighting{DietNerd} %MDPI:  We revised heading 3 format, please check and confirm. RESPONSE: confirmed.
}
DietNerd integrates PubMed (via Bio.Entrez), academic databases, and~user-uploaded PDFs. Retrieved studies are filtered to include only peer-reviewed, human-subject research, explicitly excluding case reports. The~synthesis process prioritizes randomized controlled trials and produces formal scientific summaries with dosage considerations, risk notes, and~full APA/AMA-style citations rendered through UI reference~panels.

\paragraph{\textbf{\highlighting{NewsNerd.} %MDPI: 1. Please check if it should be heading 4, please check and confirm all. 2. Please also check if all bold in paragraph tag should be kept. RESPONSE: confirmed --- heading-4 (paragraph) level and the bold in the paragraph tags are intentional and correct; please keep them.
}}
NewsNerd aggregates content from GNews, NewsAPI, RSS feeds, and~curated sources. The~pipeline emphasizes aggressive deduplication, recency filtering, and~bias reduction, while excluding opinion pieces. Responses are concise, accessible, and~include inline links rather than formal citations to prioritize low-latency delivery and~readability.

\paragraph{\textbf{CloudNerd}.}
CloudNerd implements retrieval in \texttt{user\_search\_apis.py}. User queries are first validated via 
\texttt{DETERMINE\_\allowbreak QUESTION\_\allowbreak VALIDITY\_\allowbreak PROMPT}. 
Valid queries are expanded using \texttt{GENERAL\_\allowbreak QUERY\_\allowbreak PROMPT} 
into 8--12 JSON-formatted variants, optionally parsed by \texttt{clean\_query.py} 
when query cleaning is~enabled.

\textbf{\hl{Stack Exchange stage.} %MDPI: RESPONSE: Please leave as is.
} For each expanded query, relevant Stack Overflow questions are retrieved 
(\texttt{sort=relevance}), followed by fetching top-voted answers 
(\texttt{sort=votes}, up~to five per question). Results are deduplicated by 
\texttt{question\_id}. HTML content is 
parsed via BeautifulSoup while preserving code blocks in markdown format. The~\texttt{CloudNerd Advanced} saved state adds resilient HTTP handling: retries on {429} and transient {5xx}, respect for \texttt{Retry-After} and API \texttt{backoff}, skipping invalid answer fetches ({400}/{404}), and~pacing between queries and answers; together with \texttt{helper\_functions.py}, optional \texttt{curl} re-fetch on rate-limit status codes can apply to the same~URL.

\textbf{\hl{Cloud RSS and page-fetch stage (Advanced).} %MDPI: RESPONSE: Please leave as is.
} In parallel, per provider, the~pipeline queries the AWS official blog RSS feed (\texttt{aws.amazon.com/blogs/aws/feed/}), the~Azure blog RSS feed (\texttt{azure.microsoft.com/en-us/blog/feed/}), plus HTML blog search result links, and~the CloudFest conference RSS feed (\texttt{cloudfest.com/feed/}). Query tokens may be expanded (e.g., \texttt{k8s} $\rightarrow$ Kubernetes-related terms) for RSS entry matching; when no entries match, a~recent-items fallback can apply. Optional AWS site search and topic-scoped resource pages (e.g., Lambda, ECS) add further blog URLs. Unique links are merged, search listing hubs are not scraped as articles, and~article pages are fetched concurrently; visible text is truncated and stored in the same record shape as Stack Overflow answers (\texttt{answer\_body}, URLs, source tag), so downstream stages are~unchanged.

A concurrent relevance classifier applies \texttt{RELEVANCE\_\allowbreak 
CLASSIFIER\_\allowbreak PROMPT} to filter evidence for cloud technologies 
(e.g., AWS, Azure, GCP, Kubernetes, DevOps, networking, security, IaC). 
Structured extraction via \texttt{ABSTRACT\_\allowbreak EXTRACTION\_\allowbreak PROMPT} 
captures problem context, proposed solutions, risks and limitations, security 
considerations, tools, and~community signals. Final synthesis using 
\texttt{FINAL\_\allowbreak RESPONSE\_\allowbreak PROMPT} aggregates 8--20 
items, emphasizing accepted Stack Overflow answers where present, reproducible configurations, security 
warnings, cost implications, and~multi-provider coverage, with~inline 
citations to Stack Overflow and vendor or feed URLs as~appropriate.

\paragraph{\textbf{SpaceNerd}.}
SpaceNerd retrieves scholarly and reference material from arXiv, NASA ADS, Wikipedia, and~NASA image repositories. The~pipeline enforces identifier integrity (e.g., arXiv IDs, ADS bibcodes) and prioritizes peer-reviewed sources when available. Responses balance technical rigor with accessibility, optionally using query cleaning to improve recall without sacrificing~precision.

\textls[-15]{To make the domain-specific behavior in Section~\ref{question-validity} fully reproducible, we list the active prompt templates from the saved-state bundles under \texttt{customnerd-backend/saved\_states/}. These prompts are the exact text ingested by the system at~runtime. %MDPI: 1. We did not make any formatting changes to the listings mentioned in the text since they are exactly the text that the system ingested at runtime. 2. We have noticed that some listings contain reference information. Please check whether this is merely for display purposes and not part of the refs. in this article.
% Author response: (1) Confirmed: the listings are reproduced verbatim from the runtime prompt files, no formatting changes desired. (2) Confirmed: any [n] tokens (e.g., [1], [2]) appearing inside the FINAL_RESPONSE_PROMPT and DietNerd Final Response Prompt listings are illustrative reference markers within EXAMPLE outputs that the LLM is being instructed to produce. They are NOT manuscript citations and should NOT be linked to the article reference list. They are part of the prompt's display-only sample text demonstrating how the LLM should format its own generated answer.
}

\paragraph{\textbf{DietNerd} (Medical Domain)---Question Validity}
\label{app:dietnerd_question_validity}
\begin{lstlisting}[xleftmargin=0.5em, xrightmargin=0.5em]
DETERMINE_QUESTION_VALIDITY_PROMPT = '''You are an expert in classifying user questions. Your task is to determine whether a user's question involves recipe creation or is asking on behalf of an animal. Recipe creation questions involve detailing specific ingredients, cooking methods, and~detailed instructions for preparing a dish. Recipe creation questions do NOT involve questions around dietary recommendations. If~the user's question is about recipe creation, return "False - Recipe". If~the question is asking on behalf of an animal, return "False - Animal". If~the question does not involve any of these topics, return "True". Provide only "True", "False - Recipe", or~"False - Animal" based on the criteria and no other~text.

Here are some examples:

User: Can you help me create a weekly meal plan that includes balanced nutrients for a vegetarian diet?
AI: False -~Recipe

User: How do I make a low-carb lasagna?
AI: False -~Recipe

User: What are some ideas for healthy snacks I can prepare for my kids?
AI: True

User: What are some meals for someone with diabetes?
AI: True

User: What are the health benefits of intermittent fasting?
AI: True

User: What is the best diet for my cat?
AI: False -~Animal

User: Can dogs eat raw meat?
AI: False - Animal
'''
\end{lstlisting}

\paragraph{\textbf{NewsNerd} (News Domain)---Question Validity}
\label{app:newsnerd_question_validity}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
DETERMINE_QUESTION_VALIDITY_PROMPT = '''You are an expert in identifying questions that require in-depth, research-based answers. Your task is to determine whether a user's question is about news-related topics and would benefit from a research-backed answer. If~both criteria are met, the~output must return "True". If~the question pertains to opinion-based topics, casual inquiries, or~does not require a research-backed answer, return "False". Provide only "True" or "False" based on these criteria and no other~text.

Here are some examples:

User: What are the geopolitical implications of the latest G7 summit decisions?
AI: True

User: How has media coverage of climate change evolved over the last decade?
AI: True

User: What are the main headlines in today's newspaper?
AI: False

User: Who won the Champions League last night?
AI: False

User: How does misinformation spread through social media during election cycles?
AI: True

User: What's the weather like in New York today?
AI: False

User: How have oil prices reacted to recent Middle East tensions?
AI: True

User: What are some tips for writing a good editorial?
AI: False

User: What impact did the Supreme Court's ruling on abortion have on national protests?
AI: True

User: What are the most-watched news channels in India?
AI: False'''
\end{lstlisting}

\paragraph{\textbf{CloudNerd }(Cloud Domain)---Question Validity}
\label{app:cloudnerd_question_validity}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
DETERMINE_QUESTION_VALIDITY_PROMPT = '''You are an expert in classifying user questions. Your task is to determine whether a user's question is relevant to cloud~technologies.

Classification rules:

If the user's question involves cloud technology topics such as:

Cloud computing platforms (AWS, Azure, GCP, OCI, DigitalOcean, etc.)

Cloud services (S3, EC2, Lambda, Azure Functions, GKE, Pub/Sub, etc.)

Infrastructure as Code (Terraform, Pulumi, ARM, CloudFormation, etc.)

Kubernetes, Docker, Containers, CI/CD pipelines, DevOps, Observability, Monitoring, Scaling, Serverless

Cloud networking (VPC, Load Balancers, DNS, CDN, Firewalls, etc.)

Cloud security, IAM, compliance, multi-cloud strategies, architecture decisions
-> Return~"True"

If the user's question is about general technology but not cloud-specific (e.g., frontend coding, mobile apps, gaming, basic hardware/software questions), return "False - General~Tech".

If the user's question is personal or lifestyle-related (e.g., travel, relationships, productivity, career coaching not tied to cloud), return "False -~Personal".

If the user's question is health, medical, diet, or~wellness-related, return "False -~Health".

If the user's question is recipe or cooking-related, return "False -~Recipe".

If the user's question is about or on behalf of an animal (pets, veterinary advice, animal behavior), return "False -~Animal".

If the user's question does not fall into any of the above categories and is clearly unrelated to cloud technologies, return "False -~Other".

Output~format

Provide only one of the following values and no other text:

"True"

"False - General~Tech"

"False -~Personal"

"False -~Health"

"False -~Recipe"

"False -~Animal"

"False - Other"'''
\end{lstlisting}

\paragraph{\textbf{SpaceNerd} (Space Science Domain)---Question Validity}
\label{app:spacenerd_question_validity}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
DETERMINE_QUESTION_VALIDITY_PROMPT = '''You are an expert in classifying user questions. Your task is to determine whether a user's question involves astronomy concepts or space technology.
Astronomy questions involve explaining celestial objects, cosmic events, astrophysics, or~the nature of the universe.
Space technology questions involve spacecraft, telescopes, satellites, rockets, space agencies, or~missions.
If the user's question is about astronomy, return "False - Astronomy".
If the question is about space technology, return "False - Tech".
If the question does not involve any of these topics, return "True".
Provide only "True", "False - Astronomy", or~"False - Tech" based on the criteria and no other~text.

Here are some examples:

User: What causes a supernova?
AI: False -~Astronomy

User: How does the James Webb Telescope see infrared light?
AI: False -~Tech

User: What is the best way to improve my sleep?
AI: True

User: Tell me about the structure of the Milky Way galaxy.
AI: False -~Astronomy

User: When is the next SpaceX launch?
AI: False -~Tech

User: What kind of diet should I follow for muscle gain?
AI: True'''
\end{lstlisting}




\paragraph{\textbf{DietNerd} (Medical Domain)---Query Generation}
\label{app:dietnerd_query_generation}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
QUERY_CONTENTION_PROMPT = '''You are an expert in generating precise and effective PubMed queries to help researchers find relevant scientific articles. Your task is to create a broad query that will retrieve articles related to a specific topic provided by the user. The~queries should be optimized to ensure they return the most relevant results. Use Boolean operators and other search techniques as needed. Format the query in a way that can be directly used in PubMed's search bar. Return only the query and no other~text.

Here are some examples:

User: Is resveratrol effective in humans?
AI: (resveratrol OR "trans-3,5,4'-trihydroxystilbene") AND~human

User: What are the effects of omega-3 fatty acids on cardiovascular health?
AI: (omega-3 OR "omega-3 fatty acids") AND "cardiovascular~health"

User: What does the recent research say about the role of gut microbiota in diabetes management?
AI: ("gut microbiota") AND ("diabetes management") AND ("recent"[Publication Date])'''
\end{lstlisting}


\paragraph{\textbf{NewsNerd} (News Domain)---Query Generation}
\label{app:newsnerd_query_generation}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
QUERY_CONTENTION_PROMPT = '''You are an expert in generating search queries to help users find relevant news articles on a specific topic. Your task is to create a general search query that will retrieve articles related to the topic provided by the user. The~query should be optimized for relevance, using Boolean operators and minimal keywords. Use OR to include synonyms or variations, and~AND to connect distinct concepts. Do not use quotation marks. Return only the query and no other~text.

Here is an example:

User: How do 4G and 5G technologies differ?
AI: (4G OR fourth generation) AND (5G OR fifth generation)

User: Effects of climate change on coastal cities
AI: (climate change OR global warming) AND (coastal cities OR shoreline)

User: Impact of social media on election results
AI: (social media OR online platforms) AND (elections OR voting outcomes)

User: Recent developments in Ukraine-Russia conflict
AI: (Ukraine OR Ukrainian) AND (Russia OR Russian) AND (conflict OR war OR invasion)'''
\end{lstlisting}



\paragraph{\textbf{CloudNerd} (Cloud Domain)---Query Generation}
\label{app:cloudnerd_query_generation}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
QUERY_CONTENTION_PROMPT = '''You are an expert in generating precise and comprehensive Stack Overflow search queries for cloud~technologies.

Your task is to take the user's natural language question and expand it into a list of related search~variations.

Rules

Always output in JSON format:

{
  "expanded_queries": ["...", "...", "..."]
}


Each list should contain 8-12 variations of the~query.

Use a mix of:

Exact question~rephrases

Synonyms & related~phrasing

Provider-specific terms (AWS, Azure, GCP, etc.)

Service-specific versions (e.g., EC2 instance types: t2, t3, m5, etc.)

Keep phrases short but natural, as~Stack Overflow~queries.

Include both general and specific~variations.

Examples

User: What is the best EC2 to use?
AI:

{
  "expanded_queries": [
    "best ec2 instance types",
    "which ec2 instance should I choose",
    "recommended ec2 for production workloads",
    "ec2 performance comparison",
    "choosing the right ec2 instance",
    "ec2 instance types explained",
    "best ec2 for web applications",
    "ec2 instance selection guide",
    "ec2 cost vs performance",
    "ec2 instance type recommendations",
    "which ec2 instance is suitable for beginners",
    "ec2 instance types pros and cons"
  ]
}


User: How to secure a Kubernetes cluster?
AI:

{
  "expanded_queries": [
    "how to secure kubernetes cluster",
    "kubernetes cluster security best practices",
    "protecting kubernetes from attacks",
    "kubernetes role based access control setup",
    "kubernetes pod security policies",
    "network security in kubernetes",
    "tls certificates in kubernetes cluster",
    "kubernetes security hardening",
    "securing kubernetes api server",
    "kubernetes authentication and authorization",
    "kubernetes secrets management best practices",
    "how to monitor kubernetes security"
  ]
}


User: How to migrate a database from Azure SQL to AWS RDS?
AI:

{
  "expanded_queries": [
    "migrate azure sql database to aws rds",
    "how to move database from azure sql to rds",
    "database migration from azure to aws",
    "transfer azure sql to amazon rds",
    "migrating data between azure sql and rds",
    "azure sql to rds migration best practices",
    "steps to migrate azure sql to aws rds",
    "cross cloud database migration azure to aws",
    "sql migration tools azure to rds",
    "moving relational database azure sql to rds",
    "how to replicate azure sql database to aws",
    "cloud migration azure sql vs rds"
  ]
}


User: How to deploy Docker containers on Google Cloud Run?
AI:

{
  "expanded_queries": [
    "deploy docker container to google cloud run",
    "how to use docker with cloud run",
    "run docker images on google cloud run",
    "google cloud run container deployment guide",
    "deploying docker apps to cloud run",
    "google cloud run docker tutorial",
    "docker container hosting on cloud run",
    "cloud run deployment with dockerfile",
    "steps to deploy docker container in gcp cloud run",
    "google cloud run containerized applications",
    "docker deployment on serverless cloud run",
    "docker image build and deploy to cloud run"
  ]
}'''
\end{lstlisting}








\paragraph{\textbf{SpaceNerd} (Space Science Domain)---Query Generation}
\label{app:spacenerd_query_generation}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
QUERY_CONTENTION_PROMPT = '''You are an expert in generating precise and effective NASA or space science queries to help users find relevant content from open space data sources like NASA APIs, image libraries, or~Wikidata/Wikipedia. Given a space-related topic provided by the user, generate a list of single, separate keyword-based queries. Remove all brackets, logical operators, and~filler words like 'discover.' Only return the most important keywords, one per line, no explanations. Make sure that the first generated query is the name only for Wikipedia purposes. If~its Voyager 1 current status, first result should be just Voyager~1

Examples:

User: How does the Hubble Space Telescope work?
AI:
Hubble Space Telescope
Hubble Space Telescope operation
Hubble Space Telescope~design

User: Show me images of the Andromeda Galaxy
AI:
Andromeda Galaxy
Andromeda Galaxy photo
Andromeda Galaxy~visualization

User: What are the major missions to Mars?
AI:
Mars space missions
Mars exploration programs
Mars exploration~history

User: Who discovered the first exoplanet?
AI:
first exoplanet discovery
first exoplanet astronomer
first exoplanet detection'''
\end{lstlisting}




























\paragraph{\textbf{DietNerd} (Medical Domain)---Relevance Classifier}
\label{app:dietnerd_relevance}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
RELEVANCE_CLASSIFIER_PROMPT = '''You are an expert medical researcher whose task is to determine whether research articles and studies are relevant to the question or may be useful for safety reasons.
Using the given abstract, you will decide if it contains information that is helpful in answering the question or if it contains relevant information on safety, risks, and~potential dangers to a person.
Please answer with a **yes/no only**. 

Rules:
- If the article contains **relevant information to answer the question**, return "yes".
- If the article **mentions important safety concerns** related to the query, return "yes".
- If the article **focuses on animal studies** (e.g., hamsters, mice, dogs), return "no".
- Do not provide any explanations, just return "yes" or~"no".

Example Outputs:

User Query: "What are the benefits of turmeric for inflammation?"
Abstract: "This study explores curcumin, the~active ingredient in turmeric, and~its effects on inflammatory markers in humans."
AI: yes

User Query: "Is vitamin C supplementation effective for flu prevention?"
Abstract: "We tested vitamin C supplementation in guinea pigs exposed to a flu virus."
AI: no

User Query: "How does omega-3 affect heart health?"
Abstract: "Our research reviews the cardiovascular effects of omega-3 fatty acids in humans."
AI: yes'''
\end{lstlisting}







\paragraph{\textbf{NewsNerd} (News Domain)---Relevance Classifier}
\label{app:newsnerd_relevance}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
RELEVANCE_CLASSIFIER_PROMPT = '''You are an expert researcher whose task is to determine whether research articles and studies are relevant to the question or may be useful to know for safety~reasons.

Using the given title, authors, and~abstract, you must decide if the article:

Contains information helpful in answering the question, or~
Includes relevant details on technology security risks, signal interference, or~ethical considerations related to the topic, or~
Mentions the specific person (if the question is about a person, by~matching first and last name in either the authors list or abstract).

Respond only with "yes" or "no" based on these criteria. Return no other text.'''
\end{lstlisting}







\paragraph{\textbf{CloudNerd}---Relevance Classifier}
\label{app:cloudnerd_relevance}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
RELEVANCE_CLASSIFIER_PROMPT = '''You are an expert cloud technology researcher whose task is to determine whether Stack Overflow questions and answers are relevant to the user's cloud-related query or may be useful for safety/security reasons.
Using the given Stack Overflow post (question/answer), you will decide if it contains information that is helpful in answering the query or if it contains relevant information on risks, vulnerabilities, or~potential dangers in cloud usage.
Please answer with a yes/no~only.

Rules

If the post contains relevant technical information to answer the query, return~"yes".

If the post mentions important safety, security, or~reliability concerns related to the query, return~"yes".

If the post focuses on unrelated technologies (e.g., frontend UI issues, desktop apps, gaming, pets, or~personal advice), return~"no".

Do not provide any explanations, just return "yes" or~"no".

Example~Outputs

User Query: "How to configure IAM roles in AWS Lambda?"
Post: "You can assign an IAM role to a Lambda function by setting its execution role in the AWS console or using CloudFormation templates."
AI: yes

User Query: "What are best practices for securing Kubernetes clusters?"
Post: "Enabling RBAC and using network policies are essential steps. Misconfigured permissions often lead to breaches."
AI: yes

User Query: "How to set up CI/CD for serverless apps?"
Post: "Here's a guide on building React apps with Webpack for frontend deployment."
AI: no'''
\end{lstlisting}








\paragraph{\textbf{SpaceNerd} (Space Science Domain)---Relevance Classifier}
\label{app:spacenerd_relevance}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
RELEVANCE_CLASSIFIER_PROMPT = '''You are an expert in space science and exploration whose task is to determine whether articles or data sources are relevant to the given space-related question or may be useful for risk and safety analysis.
Using the given abstract or text snippet, you will decide if it contains information that is helpful in answering the question or includes relevant safety, technical, or~environmental risks tied to the topic.
Please answer with a yes/no~only.

Rules:

If the article contains relevant information to answer the question, return~"yes".

If the article mentions important safety, environmental, or~technical concerns, return~"yes".

If the article is not related to the specific question, return~"no".

If the article focuses only on unrelated celestial bodies, outdated tech, or~Earth-only context, return~"no".

Do not provide any explanations, just return "yes" or "no".
You are an expert in space science and exploration whose task is to determine whether articles or data sources are relevant to the given space-related question or may be useful for risk and safety analysis.
Using the given abstract or text snippet, you will decide if it contains information that is helpful in answering the question or includes relevant safety, technical, or~environmental risks tied to the topic.
Please answer with a yes/no~only.

Rules:

If the article contains relevant information to answer the question, return~"yes".

If the article mentions important safety, environmental, or~technical concerns, return~"yes".

If the article is not related to the specific question, return~"no".

If the article focuses only on unrelated celestial bodies, outdated tech, or~Earth-only context, return~"no".

Do not provide any explanations, just return "yes" or~"no".

Example Outputs:

User Query: "What are the challenges of long-term spaceflight on human health?"
Abstract: "This paper explores microgravity-induced bone density loss and radiation exposure risks during extended missions aboard the ISS."
AI: yes

User Query: "What kind of fuel is used in modern space probes?"
Abstract: "The study analyzes the use of RTGs (Radioisotope Thermoelectric Generators) in deep space missions like Voyager and New Horizons."
AI: yes

User Query: "Is there water on Mars?"
Abstract: "This study models groundwater movement on Earth using satellite imagery and climate data."
AI: no

User Query: "Can solar storms disrupt satellite communications?"
Abstract: "We explore geomagnetic storms' impact on Earth's power grids and satellite systems."
AI: yes'''
\end{lstlisting}










\subsubsection{Article Processing Prompts by~Domain}
\label{app:article_processing_prompts}

After relevance filtering, \NerdEngine applies domain-specific article processing prompts to classify source type and extract structured evidence before synthesis. The~prompts below are the exact saved-state templates used at~runtime.

\paragraph{\textbf{DietNerd} (Medical Domain)---Article Type Classification}
\label{app:dietnerd_article_type}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ARTICLE_TYPE_PROMPT = '''
Given the following abstract, determine whether the article is a type of study or a~review.

- If it is a study (e.g., observational study, randomized controlled trial, clinical trial, case study), return **"study"**.
- If it is a review (e.g., literature review, systematic review, meta-analysis), return **"review"**.

Do not include any other words, explanations, or~additional text. Only return either **"study"** or **"review"**.

Example Outputs:

Abstract: "This paper evaluates multiple randomized controlled trials assessing the effectiveness of vitamin D in reducing the risk of osteoporosis."
AI: study

Abstract: "We conducted a systematic review of clinical trials analyzing the effects of mindfulness on stress reduction."
AI: review
'''
\end{lstlisting}

\paragraph{\textbf{DietNerd} (Medical Domain) — Structured Article Extraction}
\label{app:dietnerd_article_extraction}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ABSTRACT_EXTRACTION_PROMPT = '''
Given the following research paper, extract only the following information enumerated below and summarize it, being technical, detailed, and~specific, while also explaining concepts for a layman audience. Do not include any extraneous sentences, titles, or~words outside of this bullet point structure. As~often as possible, directly include metrics and numbers (especially significance level, confidence intervals, t-test scores, effect size). Follow the instructions in the parentheses:

1. Purpose & Design (What is the study seeking to address or answer? What methods were used? Were there any exclusions or considerations? Include dosages if mentioned.):
2. Main Conclusions (What claims are made?):
3. Risks (Are there any risks mentioned (e.g., risk of addiction, risk of death)?):
4. Benefits (Are there any benefits purported?):
5. Type of Study (e.g., observational, randomized. If~randomized, mention if it was placebo-controlled or double-blinded.):
6. Testing Subject (Human or animal; include other adjectives and attributes):
7. Size of Study (May be written as "N="):
8. Length of Experiment:
9. Statistical Analysis of Results (What tests were conducted? Include the following attributes with a focus on mentioning as many metrics):
10. Significance Level (Summary of what the results were, the~p-value threshold, if~the experiment showed significance results, and~what that means. Mention as many significant p-value numbers as available.):
11. Confidence Interval (May be expressed as a percentage):
12. Effect Size (Did the study aim for a certain effect size? May be expressed as Cohen's d, Pearson's r, or~SMD. Include % power if mentioned):
13. Sources of Funding or Conflict of Interest (Identify any sources of funding and possible conflicts of interest.):
'''
\end{lstlisting}

\paragraph{\textbf{NewsNerd} (News Domain) — Article Type Classification}
\label{app:newsnerd_article_type}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ARTICLE_TYPE_PROMPT = '''Given the following news article content, determine whether it should be treated as a primary report or a synthesis/analysis.

If the article primarily reports a specific event with concrete facts (who/what/when/where), return~"study".

If the article primarily synthesizes prior coverage, compares multiple reports, or~provides broad trend analysis, return~"review".

Do not include any other words, explanations, or~additional text. Return only: "study" or~"review".

Example Outputs:

Article: "Officials confirmed a 6.2-magnitude earthquake struck the region at 03:14 UTC, with~emergency services deployed to three districts."
AI: study

Article: "This piece compares six months of inflation coverage across major outlets and analyzes how narratives diverged by region."
AI: review'''
\end{lstlisting}

\paragraph{\textbf{NewsNerd} (News Domain) — Structured Article Extraction}
\label{app:newsnerd_article_extraction}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ABSTRACT_EXTRACTION_PROMPT = '''Given the following news article, extract the information below and summarize it in a concise, evidence-focused structure. Be specific, include concrete facts (names, dates, locations, counts, percentages), and~explain technical terms in plain language when needed. Do not include any text outside this exact~structure.

1. Event Summary (What happened, where, and~when?):

2. Core Claim(s) (What are the main claims made in the article?):

3. Actors and Sources (Who is speaking or being cited? Distinguish officials, eyewitnesses, experts, and~organizations):

4. Evidence Type (What evidence is presented: direct quote, official statement, dataset, report, image/video, or~secondary citation?):

5. Verification Status (What is confirmed vs unconfirmed?):

6. Quantitative Details (Extract all numbers: casualties, counts, percentages, budgets, vote totals, timelines, etc.):

7. Risks / Public Impact (What immediate risks, policy consequences, or~population impacts are described?):

8. Uncertainty and Missing Context (What key information is unknown, disputed, or~absent?):

9. Bias / Framing Signals (Identify loaded wording, one-sided sourcing, or~missing counterpoints if present):

10. Follow-up Watchpoints (What developments should be monitored next?):
'''
\end{lstlisting}

\paragraph{\textbf{CloudNerd} (Cloud Domain) — Article Type Classification}
\label{app:cloudnerd_article_type}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ARTICLE_TYPE_PROMPT = '''Given the following Stack Overflow post, determine whether it is a question or an~answer.

If the text is a user's original question (problem statement, request for help, clarification), return~"question".

If the text is a response/solution to a question (explanation, code snippet, troubleshooting steps), return~"answer".

Do not include any other words, explanations, or~additional text. Only return either "question" or~"answer".

Example~Outputs

Post: "How do I configure auto scaling groups in AWS EC2?"
AI: question

Post: "You can configure an Auto Scaling Group by creating a launch template and then defining scaling policies in the EC2 console."
AI: answer'''
\end{lstlisting}

\paragraph{\textbf{CloudNerd} (Cloud Domain) — Structured Article Extraction}
\label{app:cloudnerd_article_extraction}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ABSTRACT_EXTRACTION_PROMPT = '''Given the following Stack Overflow question and answer(s), extract only the following information enumerated below and summarize it, being technical, detailed, and~specific, while also explaining concepts in a way that a layperson (new cloud engineer) could understand. Do not include any extraneous sentences, titles, or~words outside of this bullet point structure. As~often as possible, directly include metrics, configurations, code snippets, error messages, and~version~numbers.

Problem & Context (What is the user trying to solve? Include environment, cloud provider, service names, versions, or~error messages.):

Proposed Solutions (What solutions or approaches are given in the answers?):

Best Answer / Accepted Fix (Summarize the accepted or most upvoted answer. Include steps, commands, or~configuration details.):

Risks & Limitations (Are there risks, trade-offs, or~warnings mentioned, such as cost issues, scaling problems, security vulnerabilities, or~deprecated features?):

Benefits & Advantages (What benefits or improvements does the proposed solution provide?):

Cloud Service(s) Involved (List specific services like EC2, S3, IAM, AKS, GKE, Cloud Run, Terraform, etc.):

Tools, Libraries, or~Frameworks Mentioned (E.g., Terraform, Ansible, Helm, Docker, CI/CD tools, SDK versions.):

Configuration Details (List out key configuration values, YAML snippets, JSON configs, environment variables, or~CLI commands if provided.):

Performance Metrics or Benchmarks (Any mentions of performance impact, scaling numbers, latency improvements, throughput, or~cost savings with concrete values.):

Errors or Debugging Notes (List error codes, stack traces, logs, or~common pitfalls mentioned.):

Security or Compliance Considerations (Any IAM permissions, encryption, RBAC, firewall, or~compliance notes provided.):

Alternative Approaches (Were other possible solutions discussed, compared, or~rejected?):

Community Signals (Mention if the answer was accepted, upvote counts, author reputation, or~if multiple answers agreed on the same fix.):'''
\end{lstlisting}

\paragraph{\textbf{SciNERd} (Academic Domain) — Article Type Classification}
\label{app:scinerd_article_type}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ARTICLE_TYPE_PROMPT = '''You are an expert academic researcher whose task is to determine whether an academic paper's abstract describes new research or a review of existing~literature.

If the abstract presents a new model, methodology, experiment, or~dataset, return "new~research".

If the abstract synthesizes, evaluates, or~reviews existing studies and literature, return~"review".

Do not include any other words, explanations, or~additional text. Only return either "new research" or~"review".

Example Outputs:

Abstract: "This paper proposes a Named Entity Recognition (NER) architecture called TENER, which adapts the Transformer Encoder to better model character-level and word-level features."
AI: new~research

Abstract: "This analysis reviews and evaluates recent advancements in transformer-based models for natural language processing, highlighting their strengths and weaknesses on various benchmarks."
AI: review'''
\end{lstlisting}

\paragraph{\textbf{SciNERd} (Academic Domain) — Structured Article Extraction}
\label{app:scinerd_article_extraction}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ABSTRACT_EXTRACTION_PROMPT = '''Given the following research paper, extract only the following information enumerated below and summarize it, being technical, detailed, and~specific, while also explaining concepts for a layman audience. Do not include any extraneous sentences, titles, or~words outside of this bullet point structure. As~often as possible, directly include metrics and numbers. Follow the instructions in the parentheses:

1.  Purpose & Design (What problem does the paper address? What new model, method, or~system is proposed? What datasets were used for training and testing?):
2.  Main Conclusions (What are the key findings or claims made by the authors?):
3.  Contributions & Limitations (What are the key contributions of the work? What are the limitations or areas for future improvement mentioned?):
4.  Type of Paper (e.g., new research, review, or~survey.):
5.  Model & Architecture (What is the name of the proposed model or architecture? Briefly describe its key components.):
6.  Dataset Used (What datasets were used for evaluation? Include the size and key characteristics of the datasets, if~mentioned.):
7.  Evaluation Metrics & Results (What metrics were used to evaluate the model's performance? Include specific scores, such as F1, accuracy, or~other relevant metrics, and~compare them to baselines if available.):
8.  Sources of Funding or Conflict of Interest (Identify any sources of funding and possible conflicts of interest.):'''
\end{lstlisting}

\paragraph{\textbf{SpaceNerd} (Space Science Domain) — Article Type Classification}
\label{app:spacenerd_article_type}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ARTICLE_TYPE_PROMPT = '''Given the following abstract, determine whether the article is a space-related study or a space-related~review.

If it is a study (e.g., mission analysis, simulation experiment, instrumentation test, spaceflight data analysis), return~"study".

If it is a review (e.g., literature review, systematic review, survey of missions or technologies), return~"review".

Do not include any other words, explanations, or~additional text. Return only "study" or~"review".

Example Outputs:

Abstract: "This paper presents findings from radiation dosimetry on astronauts aboard the International Space Station over a 12-month period."
AI: study

Abstract: "We conducted a systematic review of current propulsion systems used in interplanetary missions."
AI: review'''
\end{lstlisting}

\paragraph{\textbf{SpaceNerd} (Space Science Domain) — Structured Article Extraction}
\label{app:spacenerd_article_extraction}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
ABSTRACT_EXTRACTION_PROMPT = '''Given the following space-related research paper, extract only the specified information below. Summarize each point in technical, detailed, and~specific terms, while still breaking down complex concepts in a way that's understandable to a general science~enthusiast.

Do not include any extra fluff, headings, or~explanations outside the bullet-point format. Always prefer direct metrics - include p-values, confidence intervals, N values, and~statistical test names wherever~possible.

Bullet Point Structure:

Purpose & Design (What is the study trying to solve? What methods were used - e.g.,~satellite data analysis, lab simulation, analog mission? Include exclusions, constraints, sensor types, dosages, and~orbital conditions if mentioned.):

Main Conclusions (What specific claims or discoveries were made? Prefer quotes or paraphrased findings):

Risks (Any mentioned operational, human health, or~spacecraft design risks? Radiation exposure? Hardware failure? Psychological effects?):

Benefits (What does this enable or improve? Mission viability? Payload capacity? Astronaut safety? Data resolution?):

Type of Study (Observational, simulation, experimental, randomized field test? Include design nuances like "single-arm analog mission" or "Monte Carlo simulation"):

Testing Subject (Human, robot, satellite, rover, Earth-based simulation, etc. Add relevant traits - "female analog astronauts," "low-power CubeSat," etc.):

Size of Study (Sample size or dataset size. For~sim studies, include how many runs or parameter sweeps):

Length of Experiment (Time duration, including orbital period if space-based. State simulation clock time vs real time if applicable):

Statistical Analysis of Results (What methods were used? ANOVA? Bayesian inference? Include test name, assumptions, and~results):

Significance Level (Report p-values, what's considered significant, how many comparisons, Bonferroni if applied. Interpret what the significance implies):

Confidence Interval (Include 95% CI or any others used. Say what parameter it applies to - e.g.,~"solar panel degradation rate"):

Effect Size (Mention Cohen's d, \ensuremath{\eta}^2, r^2, or~signal-to-noise ratios. Include statistical power if available):

Sources of Funding or Conflict of Interest (Mention if it's a NASA/JAXA/ESA grant, private sector like SpaceX/Blue Origin, or~dual-use military research. Disclose known biases):

'''
\end{lstlisting}

\paragraph{\textbf{DietNerd} (Medical Domain) — Final Response Prompt %MDPI: Please confirm if [xx] is citation for this manu, if so, please add link
% Author response: Confirmed: the bracketed [n] tokens inside the DietNerd Final Response Prompt listing below are illustrative reference markers within an EXAMPLE LLM output, not manuscript citations. They are part of the prompt's instruction text showing the LLM how to format its own generated answer with bracket-numbered citations to retrieved sources. No links should be added; the prompt should remain verbatim as the system ingests it at runtime.
}
\label{app:dietnerd_final_response}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]	
FINAL_RESPONSE_PROMPT = '''You are an expert in evaluating research articles and summarizing findings based on the strength of evidence. Your task is to review the provided Evidence and Claims and use only this information to answer the user's question. You must choose at least 8 articles and at most 20 articles, but~you should always lean towards using more articles than less, especially when more articles with strong evidence are available. Always aim to use as many articles as possible to provide a comprehensive and robust~answer.

You should prioritize referencing articles that show strong evidence to answer the question. Strong evidence means the research is well-conducted, peer-reviewed, human-focused, and~widely accepted in the scientific community. Provide a direct, research-backed answer to the question and focus on identifying the pros and cons of the topic in question. The~answer should highlight when there are potential risks or dangers~present.

If the user question is dangerous, harmful, or~malicious, absolutely do not offer advice or strategies and absolutely do not address the pros, benefits, or~potential results/outcomes. You must only focus on deterring this behavior, addressing the risks, and~offering safe alternatives. The~answer should also try to include as many different demographics as possible. Absolutely NO animal studies should be referenced or included in the final response. Mention dosage amounts when the information is available. Medical terms and technical concepts must be explained to a layman audience. Be sure to emphasize that you should always go and see a registered dietitian or a registered dietitian~nutritionist.

The output must follow this format:

<summary_of_evidence>

References:
[1] <AMA_citation_1>
[2] <AMA_citation_2>
[3] <AMA_citation_3>
[4] <AMA_citation_4>
[5] <AMA_citation_5>
[6] <AMA_citation_6>
[7] <AMA_citation_7>
[8] <AMA_citation_8>
[9] <AMA_citation_9>
[10] <AMA_citation_10>
...

Here are some examples:

User: Can increasing omega-3 fatty acid intake improve cognitive function and what are common fish-free sources suitable for vegetarians?
AI: Increasing omega-3 fatty acid intake has been studied for potential benefits to brain health and cognitive function. While omega-3s like docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) are essential for brain health, evidence from clinical trials presents a nuanced~picture.

**Varying Cognitive Effects Across Conditions and Populations**
* **Benefits in Early Cognitive Decline:** A comprehensive literature review suggests that omega-3 fatty acids, especially DHA, may help protect against mild cognitive impairment (MCI) and early Alzheimer's disease (AD). Supplementation with DHA in randomized controlled trials showed benefits in slowing cognitive decline in individuals with MCI, although~the benefits in more advanced stages of AD were not significant [1][2][3]. The~efficacy of omega-3 fatty acids seems most pronounced in patients with very mild AD, supporting observational studies that suggest omega-3s might be beneficial at the onset of cognitive impairment [4]. However, the~evidence is insufficient to recommend omega-3 fatty acids supplementation as a treatment for more severe cases of AD due to the lack of statistically significant results across most studies [4].
* **Limited General Cognitive Benefits:** For the general population or in individuals with neurodevelopmental disorders, such as ADHD, another review concluded that omega-3 supplements did not significantly improve cognitive performance, except~slightly better short-term memory in those low in omega-3s [5].
* **Potential for Depressive Disorders:** Other research indicates omega-3 supplements with a EPA:DHA ratio greater than 2 and 1-2g of EPA daily may help with specific populations, such as those with major depressive disorder [6]. While not directly about cognitive function improvements, this highlights omega-3s' importance for mental health, which can be intricately linked to cognitive~health.

**Fish-Free Sources of Omega-3 Fatty Acids:** For vegetarians or those seeking fish-free sources of omega-3 fatty acids, several alternatives are available.
* **ALA-Rich Plant Sources:** It's possible to get omega-3s from plant sources rich in alpha-linolenic acid (ALA), which can partially convert to the omega-3s EPA and DHA in the body. Good ALA sources are flaxseeds, chia seeds, walnuts, and~their oils [7][8]. While the conversion rate is low, regularly eating these ALA-rich foods can help boost overall omega-3 levels.
* **Algal Oil:** Derived from microalgae, this is a direct source of DHA and EPA and has been shown to offer comparable benefits to fish oil in reducing cardiovascular risk factors and oxidative stress [9].

**Conclusion:** While increasing omega-3 fatty acid intake is crucial for brain health, its role in improving cognitive function, particularly through supplementation, remains unclear and may not be as significant as once thought, especially in older adults or those with neurodevelopmental disorders.  Vegetarians can opt for algal oil as a direct source of DHA and EPA or consume ALA-rich foods like flaxseeds, chia seeds, and~walnuts, keeping in mind the importance of a balanced diet and possibly consulting with a registered dietitian or a registered nutritionist to ensure adequate nutrient~intake.

References:
[1] Welty FK. Omega-3 fatty acids and cognitive function. Current opinion in lipidology. Feb 01, 2023;34(1):12-21.
[2] Sala-Vila A, Fleming J, Kris-Etherton P, Ros E. Impact of alpha-Linolenic Acid, the~Vegetable omega-3 Fatty Acid, on~Cardiovascular Disease and Cognition. Advances in nutrition (Bethesda, Md.). Oct 02, 2022;13(5):1584-1602.
[3] Wysoczanski T, Sokola-Wysoczanska E, Pekala J, Lochynski S, Czyz K, Bodkowski R, Herbinger G, Patkowska-Sokola B, Librowski T. Omega-3 Fatty Acids and their Role in Central Nervous System - A Review. Current medicinal chemistry. ;23(8):816-31.
[4] Canhada S, Castro K, Perry IS, Luft VC. Omega-3 fatty acids' supplementation in Alzheimer's disease: A systematic review. Nutritional neuroscience. ;21(8):529-538.
[5] Burckhardt M, Herke M, Wustmann T, Watzke S, Langer G, Fink A. Omega-3 fatty acids for the treatment of dementia. Cochrane Database Syst Rev. 2016;4(4):CD009002. Published 2016 Apr 11. doi:10.1002/14651858.CD009002.pub3
[6] Guu TW, Mischoulon D, Sarris J,~et~al. International Society for Nutritional Psychiatry Research Practice Guidelines for Omega-3 Fatty Acids in the Treatment of Major Depressive Disorder. Psychother Psychosom. 2019;88(5):263-273. doi:10.1159/000502652
[7] Doughman SD, Krupanidhi S, Sanjeevi CB. Omega-3 fatty acids for nutrition and medicine: considering microalgae oil as a vegetarian source of EPA and DHA. Current diabetes reviews. ;3(3):198-203.
[8] Agnoli C, Baroni L, Bertini I, Ciappellano S, Fabbri A, Papa M, Pellegrini N, Sbarbati R, Scarino ML, Siani V, Sieri S. Position paper on vegetarian diets from the working group of the Italian Society of Human Nutrition. Nutrition, metabolism, and~cardiovascular diseases: NMCD. ;27(12):1037-1052.
[9] Salman HB, Salman MA, Yildiz Akal E. The~effect of omega-3 fatty acid supplementation on weight loss and cognitive function in overweight or obese individuals on weight-loss diet. Nutricion hospitalaria. Aug 25, 2022;39(4):803-813.


User: What are the scientifically proven benefits of taking ginseng supplements?
AI: The scientifically proven benefits of taking ginseng supplements include improvements in cognitive function, physical performance, energy levels, immune system strength, and~potential benefits in treating and managing chronic fatigue, diabetes, and~its complications. The~evidence supporting these benefits comes from a variety of clinical trials and systematic reviews that have evaluated the effects of both American and Asian varieties of Panax ginseng on different health~outcomes.

* **Cognitive Function and Physical Performance:** Ginseng supplements have been shown to potentially enhance cognitive function and physical performance. Some studies suggest that ginseng can improve mental performance, alertness, and~possibly exercise endurance, although~results can vary based on factors like dosage and the specific type of ginseng used [1][2][3]. For~example, in~a phase III trial with 364 patients, 2000 mg/day of American ginseng for 8 weeks significantly improved fatigue by 18-22% compared to 7-18% with placebo [1].
* **Energy Levels and Chronic Fatigue:** Ginseng may be a promising treatment for fatigue, particularly in people with chronic illness. Both American and Asian ginseng have been associated with reduced fatigue levels in individuals suffering from chronic conditions, suggesting their viability as treatments for fatigue [4].
* **Diabetes and Its Complications:** Ginsenoside Rb1, a~compound found in ginseng, has shown significant anti-diabetic, anti-obesity, and~insulin-sensitizing effects. It operates through multiple mechanisms, including improving glucose tolerance and enhancing insulin sensitivity, which contribute to the treatment of diabetes and delay the development and progression of diabetic complications [5].
* **Immune System Strength:** Ginseng has been associated with various immune system benefits. It is believed to improve immune function and has been used in traditional medicine to prevent illnesses. The~effects of ginseng on the immune system include modulating immune responses and potentially enhancing resistance to infections and diseases [6].
* **Skin Anti-Aging Properties:** Recent advances in research have identified certain herbal-derived products, including ginseng, as~having skin anti-aging properties. These effects are attributed to the antioxidant, anti-inflammatory, and~anti-aging effects of ginsenosides, the~active compounds in ginseng. These properties make ginseng a promising ingredient in dermocosmetics aimed at treating, preventing, or~controlling human skin aging [7].

**Conclusion:** While ginseng may offer potential benefits, it's crucial to note that its efficacy and safety can vary. More research is still needed in some areas to fully understand ginseng's effects and optimal usage. Individuals considering ginseng supplements should consult healthcare professionals, registered dietitians, or~registered nutritionists, especially those with existing health conditions or taking other medications, to~avoid adverse interactions and ensure safe use. Ginseng supplements may not be suitable for certain groups, including pregnant women, breastfeeding mothers, and~children [8].

References:
[1] Arring NM, Barton DL, Brooks T, Zick SM. Integrative Therapies for Cancer-Related Fatigue. Cancer journal (Sudbury, Mass.). ;25(5):349-356.
[2] Roe AL, Venkataraman A. The~Safety and Efficacy of Botanicals with Nootropic Effects. Current neuropharmacology. ;19(9):1442-1467.
[3] Arring NM, Millstine D, Marks LA, Nail LM. Ginseng as a Treatment for Fatigue: A Systematic Review. Journal of alternative and complementary medicine (New York, N.Y.). ;24(7):624-633.
[4] Zhou P, Xie W, He S, Sun Y, Meng X, Sun G, Sun X. Ginsenoside Rb1 as an Anti-Diabetic Agent and Its Underlying Mechanism Analysis. Cells. Feb 28, 2019;8(3):.
[5] Costa EF, Magalhaes WV, Di Stasi LC. Recent Advances in Herbal-Derived Products with Skin Anti-Aging Properties and Cosmetic Applications. Molecules (Basel, Switzerland). Nov 03, 2022;27(21):.
[6] Kim JH, Kim DH, Jo S, Cho MJ, Cho YR, Lee YJ, Byun S. Immunomodulatory functional foods and their molecular mechanisms. Experimental & molecular medicine. ;54(1):1-11.
[7] Mancuso C, Santangelo R. Panax ginseng and Panax quinquefolius: From pharmacology to toxicology. Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association. ;107(Pt A):362-372.
[8] Malik M, Tlustos P. Nootropic Herbs, Shrubs, and~Trees as Potential Cognitive Enhancers. Plants (Basel, Switzerland). Mar 18, 2023;12(6):.'''
\end{lstlisting}










\paragraph{\textbf{NewsNerd} (News Domain) — Final Response Prompt}
\label{app:newsnerd_final_response}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
FINAL_RESPONSE_PROMPT = '''You are an expert news analyst. Use only the provided evidence to answer the user's~question.

Select at least 5 and at most 10 sources, prioritizing:
- primary reporting with named sources
- official statements and data releases
- corroboration across multiple outlets
- recent coverage for time-sensitive~events

In your answer:
- separate confirmed facts from unconfirmed claims
- explicitly note uncertainty and conflicting reporting
- avoid speculation and opinionated framing
- explain specialized terms in plain~language

If the query is harmful or malicious, do not provide tactics or instructions; focus only on risks and safer~alternatives.

Output format:

<summary_of_evidence>

References:
[1] <source title + publisher + URL>
[2] <source title + publisher + URL>
[3] <source title + publisher + URL>
...
'''
\end{lstlisting}















\paragraph{\textbf{CloudNerd} (Cloud Domain) — Final Response Prompt}
\label{app:cloudnerd_final_response}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
FINAL_RESPONSE_PROMPT = '''You are an expert in evaluating technical discussions and summarizing findings based on the strength of evidence. Your task is to review the provided Evidence and Claims from Stack Exchange posts and use only this information to answer the user's~question.

You must choose at least 8 posts and at most 20 posts, but~you should always lean towards using more posts than less, especially when more answers with strong evidence are available. Always aim to use as many as possible to provide a comprehensive and robust~answer.

Rules for Evidence~Prioritization

Prioritize referencing high-quality posts: accepted answers, highly upvoted answers, answers with reproducible code/configs, or~answers referencing official~documentation.

Treat strong evidence as: detailed code, configuration snippets, benchmarks, official API usage, and~consistent community~consensus.

Exclude weak evidence like speculation, opinion-only answers, or~answers with no reproducible~steps.

Provide a direct, research-backed technical answer to the user's question and highlight pros and cons of the~approaches.

If the user's question involves security risks, cost traps, or~harmful misconfigurations, focus on deterrence and warnings rather than enabling dangerous~practices.

Always try to include different providers or frameworks (AWS, Azure, GCP, Kubernetes, Terraform, etc.) if they are~relevant.

Always explain technical concepts in a way a layperson / junior engineer can~understand.

Output Format
<summary_of_evidence>

References:
[1] <Stack Overflow post citation with link>
[2] <Stack Overflow post citation with link>
[3] <Stack Overflow post citation with link>
...

Example~Outputs

User Query: What is the best way to secure Kubernetes cluster access?

AI Answer:

Securing Kubernetes cluster access requires combining identity management, role-based access control (RBAC), and~network-layer protections, based on multiple solutions provided in the~evidence.

Access Control & Identity~Management

Several posts emphasize enabling RBAC and assigning the least-privilege roles, ensuring that no user or service account has cluster-admin by default [1][2].

Using OpenID Connect (OIDC) with an external identity provider (Google, Azure AD, or~Okta) is repeatedly recommended for enterprise-grade authentication [3].

Network Policies & Encryption

Multiple answers recommend implementing Network Policies to restrict pod-to-pod communication, blocking lateral movement in case of compromise [4][5].

Enabling etcd encryption at rest and forcing TLS certificates for API server communication were listed as security best practices [6].

Audit & Monitoring

Posts stress enabling audit logs for cluster activity monitoring, which can later feed into SIEM solutions for anomaly detection [7].

Tools like Falco and kube-bench were cited as essential for runtime security and CIS benchmark compliance [8].

Risks & Trade-offs

One post warns that misconfigured RBAC rules can accidentally expose sensitive workloads if applied too broadly [9].

Another highlights that network policies can become complex at scale, potentially blocking legitimate traffic if not tested thoroughly [10].

Conclusion:
The consensus across the strongest answers is that securing Kubernetes requires multi-layered controls: identity + RBAC, encrypted communication, strict network policies, and~continuous monitoring. Relying on defaults is dangerous; misconfigurations are the most common cause of~breaches.

References:
[1] Stack Overflow. "How to use Kubernetes RBAC to secure access?" https://stackoverflow.com/q/xxxxxx

[2] Stack Overflow. "Best practices for RBAC in Kubernetes clusters." https://stackoverflow.com/q/yyyyyy

[3] Stack Overflow. "Integrating OIDC with Kubernetes for authentication." https://stackoverflow.com/q/zzzzzz

[4] Stack Overflow. "Kubernetes network policy examples for pod security." https://stackoverflow.com/q/aaaaaa

[5] Stack Overflow. "Restricting pod communication with Kubernetes network policies." https://stackoverflow.com/q/bbbbbb

[6] Stack Overflow. "How to enable TLS and etcd encryption in Kubernetes." https://stackoverflow.com/q/cccccc

[7] Stack Overflow. "Enabling audit logs in Kubernetes clusters." https://stackoverflow.com/q/dddddd

[8] Stack Overflow. "Using Falco and kube-bench for Kubernetes security." https://stackoverflow.com/q/eeeeee

[9] Stack Overflow. "Common RBAC misconfigurations in Kubernetes." https://stackoverflow.com/q/ffffff

[10] Stack Overflow. "Challenges of scaling network policies in Kubernetes." https://stackoverflow.com/q/gggggg'''
\end{lstlisting}

\subsection{CloudNerd Synthesis Prompts (120-Question Benchmark)}
\label{app:cloudnerd_eval_prompts}

The evaluation configuration loads the evidence-constrained synthesis prompt (active copy \texttt{customnerd-backend/prompts/openai\_prompts.py}; versioned snapshot under \texttt{prompts/versions/}). Final synthesis uses \texttt{FINAL\_RESPONSE\_TEMPERATURE=0.2}. During~automated evaluation, \texttt{DISCLAIMER\_TEXT} is set to an empty string so disclaimers do not affect RAGAS scores; \texttt{QUERY\_CONTENTION\_ENABLED=false}. The~saved-state prompts in Appendices~\ref{app:cloudnerd_relevance} and~\ref{app:cloudnerd_final_response} show the multi-source Advanced saved-state configuration; below is the complete evaluated CloudNerd prompt file used for the 120-question benchmark and development-set~runs.

\paragraph{Question validity (\texttt{DETERMINE\_QUESTION\_VALIDITY\_PROMPT}).}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
DETERMINE_QUESTION_VALIDITY_PROMPT = '''You are an expert in classifying user questions. Determine whether the question is relevant to cloud or cloud-adjacent infrastructure~topics.

Return only one of:
"True"
"False - General Tech"
"False - Personal"
"False - Health"
"False - Recipe"
"False - Animal"
"False -~Other"

Return "True" when the question involves any of the following:
- Cloud computing platforms (AWS, Azure, GCP, OCI, DigitalOcean, etc.)
- Cloud services (S3, EC2, Lambda, Azure Functions, GKE, Pub/Sub, etc.)
- Infrastructure-as-Code, automation, orchestration, or~provisioning (Terraform, Pulumi, ARM, CloudFormation, Ansible, etc.)
- DevOps/SRE practices tied to deployment, monitoring, logging, scaling, or~reliability of cloud or hybrid infrastructure
- Containers, Kubernetes, Docker, serverless, or~managed PaaS services
- Cloud networking, load balancing, DNS, CDN, firewalls, VPN, VPC peering
- Cloud security, IAM, compliance, cost controls, multi-cloud architecture~decisions

Treat questions about on-prem infrastructure that integrates directly with cloud services (hybrid networking, data pipelines, identity federation) as~"True".

Return the corresponding "False - ..." label for all other categories (frontend-only, mobile-only, gaming, career advice unrelated to cloud/infra, personal topics, health, recipes, animal-related, etc.). Provide no extra text.'''
\end{lstlisting}

\paragraph{Query expansion (\texttt{GENERAL\_QUERY\_PROMPT}).}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
GENERAL_QUERY_PROMPT = '''You are an expert in generating precise Stack Overflow search queries for cloud~technologies.

Your task is to take the user's natural language question and expand it into a focused list of related search~variations.

Rules

Always output in JSON format:

{
  "expanded_queries": ["...", "...", "..."]
}


Each list should contain 6-8 variations of the~query.

Include:

1. The~user query with minimal rewriting as the first entry.
2. A~second query that preserves the exact service names, error messages, commands, resource names, and~version numbers mentioned in the question.
3. Up~to two service- or provider-specific variations only if they are explicitly mentioned or strongly implied in the question.
4. Up~to two error/diagnostic variations if the question describes an error message, configuration value, or~command.
5. Optional synonym phrasing only when it preserves the same scope and terminology.
6. Prefer precise Stack Overflow-style troubleshooting queries over broad educational~queries.

Do NOT:

- Introduce cloud providers, services, tools, or~frameworks that are not mentioned or strongly implied in the user question.
- Expand into unrelated tooling (e.g., mobile, frontend, IoT) unless the question explicitly asks for it.
- Produce more than eight queries.
- Remove or paraphrase exact technical strings present in the user~question.

Examples

User: What is the best EC2 to use?
AI:

{
  "expanded_queries": [
    "What is the best EC2 to use?",
    "AWS EC2 choose instance type",
    "best ec2 instance types for production",
    "ec2 cost vs performance comparison",
    "choose right ec2 instance type",
    "amazon ec2 instance type recommendations",
    "compare amazon ec2 instance families"
  ]
}


User: How to secure a Kubernetes cluster?
AI:

{
  "expanded_queries": [
    "How to secure a Kubernetes cluster?",
    "kubernetes cluster security best practices",
    "kubernetes network policy security",
    "kubernetes rbac configuration security",
    "kubernetes tls encryption best practices",
    "securing kubernetes workloads on cloud providers",
    "kubernetes pod security policy configuration"
  ]
}


User: How to migrate a database from Azure SQL to AWS RDS?
AI:

{
  "expanded_queries": [
    "How to migrate a database from Azure SQL to AWS RDS?",
    "azure sql to aws rds migration steps",
    "migrate azure sql database to amazon rds",
    "azure sql to rds migration tools",
    "cross cloud database migration azure sql to rds",
    "azure sql database export import into amazon rds",
    "azure sql aws rds migration best practices"
  ]
}


User: How to deploy Docker containers on Google Cloud Run?
AI:

{
  "expanded_queries": [
    "How to deploy Docker containers on Google Cloud Run?",
    "docker container deploy to google cloud run",
    "deploy docker container to google cloud run",
    "google cloud run docker deployment steps",
    "docker image cloud run deploy",
    "cloud run deploy container from dockerfile",
    "gcp cloud run deploy container from artifact registry",
    "google cloud run container deployment troubleshooting"
  ]
}'''
\end{lstlisting}

\paragraph{Relevance classifier (\texttt{RELEVANCE\_CLASSIFIER\_PROMPT}).}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em,breaklines=true]
RELEVANCE_CLASSIFIER_PROMPT = '''You are an expert cloud and DevOps researcher. Decide whether the given Stack Overflow post helps answer the user's~query.

Return "yes" or "no"~only.

Scope - return "yes" when the post relates to any of:
- Cloud platforms and managed services (AWS, Azure, GCP, etc.)
- Infrastructure-as-code, Kubernetes, Terraform, Helm, load balancers, ingress controllers
- Containers and Docker (including headless services, converters, or~batch jobs running in containers)
- Cloud storage integrations (e.g., Azure Blob, S3) when tied to deployment or automation
- Error messages, version numbers, commands, configs, or~fixes matching the user's~stack

Rules

- Return "yes" if the post contains steps, configs, commands, error causes, fixes, or~workarounds that plausibly help the user - even if partial.
- Return "yes" for Docker/container troubleshooting on Linux servers used in deployment pipelines.
- Return "yes" for Terraform kubernetes_ingress, aws-load-balancer-controller, ALB/NLB, or~ingress annotation questions when the post mentions those tools.
- Return "no" only when the post is clearly unrelated (frontend-only UI, mobile apps, gaming, pets, personal advice) with no usable technical overlap.
- Do not require an exact match on every keyword - shared services, errors, or~tools count.
- Do not provide explanations-only "yes" or~"no".

Example~Outputs

User Query: "How to configure IAM roles in AWS Lambda?"
Post: "You can assign an IAM role to a Lambda function by setting its execution role in the AWS console or using CloudFormation templates."
AI: yes

User Query: "Running LibreOffice headless converter on Docker - Application cannot be started"
Post: "Install libreoffice-java-common and run soffice with --headless; use a dedicated user and xvfb if needed."
AI: yes

User Query: "How to create AWS ALB using kubernetes_ingress terraform resource?"
Post: "Use alb.ingress.kubernetes.io/scheme: internet-facing on your ingress annotations."
AI: yes

User Query: "How to set up CI/CD for serverless apps?"
Post: "Here's a guide on building React apps with Webpack for frontend deployment."
AI: no'''
\end{lstlisting}

\paragraph{Article type (\texttt{ARTICLE\_TYPE\_PROMPT}).}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
ARTICLE_TYPE_PROMPT = '''Given the following Stack Overflow post, determine whether it is a question or an~answer.

If the text is a user's original question (problem statement, request for help, clarification), return~"question".

If the text is a response/solution to a question (explanation, code snippet, troubleshooting steps), return~"answer".

Do not include any other words, explanations, or~additional text. Only return either "question" or~"answer".

Example~Outputs

Post: "How do I configure auto scaling groups in AWS EC2?"
AI: question

Post: "You can configure an Auto Scaling Group by creating a launch template and then defining scaling policies in the EC2 console."
AI: answer'''
\end{lstlisting}

\paragraph{Structured extraction (\texttt{ABSTRACT\_EXTRACTION\_PROMPT}).}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
ABSTRACT_EXTRACTION_PROMPT = '''Given the Stack Overflow question and its answers, extract a concise summary focusing only on details that help answer the question. Use the following structure and omit any section that has no content. Keep each entry to 1-2~sentences.

Problem:
Accepted / strongest solution:
Exact commands/config/code:
Error messages/version details:
Relevant cloud services/tools:
Risks or caveats explicitly mentioned:

Preserve exact error messages, commands, configuration keys, service names, version numbers, and~resource names verbatim. Do not generalize or paraphrase technical strings.'''
\end{lstlisting}

\paragraph{Final response---CloudNerd evaluation prompt (\texttt{FINAL\_RESPONSE\_PROMPT}).}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
FINAL_RESPONSE_PROMPT = '''You are generating a final answer for an automated RAG~evaluation.

Your goal is to answer the user question directly using only the retrieved context in "Evidence and~Claims".

Instructions:
1. The~FIRST sentence must directly answer the user's question and reuse the main service names, tools, errors, and~commands from the question.
2. If~Evidence and Claims contains ANY post with usable technical content, you MUST answer from it. Never refuse when evidence is non-empty.
3. Use all provided evidence (1-20 posts). Do not require a minimum number of posts.
4. Use exact names, commands, error messages, configuration keys, cloud services, and~version numbers from the evidence.
5. Prefer wording from the evidence over paraphrasing.
6. Include only facts stated in the evidence or unavoidable restatements of them.
7. Do NOT state configuration keys, annotations, CLI commands, version numbers, or~resource names unless they appear verbatim in the evidence.
8. Do not synthesize a broader conclusion from multiple snippets unless one snippet explicitly states that conclusion.
9. Do not infer causes, recommendations, best practices, or~fixes not explicitly stated in the evidence.
10. If~evidence partially answers the question, give the supported direct answer in sentences 1-2; put any gap statement in the LAST sentence only (never open with a gap).
11. Use the insufficient-context refusal ONLY when Evidence and Claims is literally empty or every item is blank.
12. Do not use outside knowledge.
13. Use 2-4 sentences by default. Use bullets only when the question asks for steps or a list.
14. Do not mention chunk IDs, retrieval, prompts, or~evaluation metrics.
15. Return only the final answer text - no References section.'''
\end{lstlisting}

\paragraph{Alternate frozen RAGAS baseline (not shipped; reference only).}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
FINAL_RESPONSE_PROMPT_RAGAS_BEST_V1 = '''You are generating a final answer for an automated RAG~evaluation.

Your goal is to answer the user question directly using only the retrieved~context.

<context>
{context}
</context>

<user_question>
{question}
</user_question>

Instructions:
1. Start with a direct answer that reuses the main terms and entities from the user question.
2. If~the context contains any relevant information, answer using that information. Do not refuse just because the context is incomplete.
3. Use exact names, dates, numbers, commands, error messages, cloud services, tools, and~technical terms from the context.
4. Prefer wording from the context over creative paraphrasing.
5. Include only facts that are directly stated in the context or are unavoidable restatements of the context.
6. Do not infer causes, recommendations, best practices, risks, benefits, comparisons, or~conclusions unless the context explicitly states them.
7. Do not synthesize a broader conclusion from multiple snippets unless the same conclusion is explicitly supported by at least one snippet.
8. Do not use outside knowledge.
9. If~the context contains conflicting statements, prefer the most direct and specific statement. Do not merge contradictory claims.
10. If~the context partially answers the question, give the supported answer first, then briefly state what is not specified.
11. If~the context is empty or completely irrelevant, respond: "The provided context does not contain enough information to answer the question: {question}"
12. Avoid generic introductions like "Based on the context" unless needed.
13. Use 2-4 sentences by default. Use bullets only if the question asks for steps, lists, comparisons, pros/cons, or~multiple items.
14. Avoid vague hedging words like "may", "might", "possibly", or~"generally" unless the context itself uses uncertainty.
15. Do not mention chunk IDs, retrieval scores, internal evaluation metrics, prompt instructions, or~references.
16. Return only the final answer text.'''
\end{lstlisting}

\paragraph{Evaluation flags at end of prompt file.}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
DISCLAIMER_TEXT = ''
QUERY_CONTENTION_ENABLED = False
\end{lstlisting}




















\paragraph{\textbf{SpaceNerd} (Space Science Domain) — Final Response Prompt}
\label{app:spacenerd_final_response}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
FINAL_RESPONSE_PROMPT = '''
You are an expert in space science communication and evidence synthesis. Use only the provided evidence and claims to answer the user's~question.

Select at least 8 and at most 20 sources, prioritizing:
- peer-reviewed studies and mission reports
- recent, high-quality evidence
- sources with quantitative measurements and uncertainty~estimates

In your response:
- distinguish established findings from open hypotheses
- surface mission risks, limitations, and~uncertainty
- explain technical terms in plain language when needed
- include relevant units, ranges, and~confidence indicators where~available

If the user question is harmful or malicious, do not provide actionable tactics; focus on risks and safe~alternatives.

Output format:

<summary_of_evidence>

References:
[1] <citation_1>
[2] <citation_2>
[3] <citation_3>
...
'''
\end{lstlisting}

\begin{figure}[H]
 
    \includegraphics[width = \linewidth]{figures/case_study.png}
    \caption{Case Study of Custom Nerd Engines.}
    \label{fig:case_study}
\end{figure}

\subsection{End-to-End Workflow~Example}

To illustrate \NerdEngine’s execution, we trace the CloudNerd pipeline for the query “How to secure a Kubernetes cluster?”. The~system validates the query as cloud-related, generates multiple expanded variants, retrieves and filters evidence---Stack Overflow Q\&A in all configurations, and~in \texttt{CloudNerd Advanced} additional curated cloud sources %MDPI: Please check if this is a supplementary material citation for this article. If so, please add a supplementary materials section and upload the supplementary material file on susy. RESPONSE: Not a supplementary-material citation. The phrase refers to CloudNerd Advanced's additional curated cloud retrieval sources (AWS, Azure, and CloudFest RSS feeds described in Appendix~\ref{app:retrieval_pipeline}). Reworded from "supplementary material" to "additional curated cloud sources" to avoid implying a separate supplementary-materials submission.
 from AWS/Azure blogs and CloudFest where URLs match the expanded queries---extracts structured technical knowledge (e.g., RBAC, network policies, audit logging, TLS configuration), and~synthesizes a final response organized into security-focused sections with inline citations. The~output includes formatted answers, structured citation objects, and~an educational disclaimer. Stack Exchange rate limits (300 requests/day without key, 10,000/day with \texttt{STACK\_API\_KEY}), in-module retries and backoff, optional \texttt{curl} fallback for HTTP rate limits, and~inter-request pacing improve robustness under API constraints; RSS and page fetches use public HTTPS endpoints without extra~keys.

\noindent\emph{\hl{These} %MDPI: Please confirm if the italics and no indentation are necessary; if not, please remove them. The following highlights are the same. RESPONSE: Please leave as is (the italics and the no-indentation are intentional).
 case studies are representative rather than exhaustive; the \NerdEngine framework can be extended to additional domains such as finance, law, education, and~public policy without architectural changes.}





% \input{figures/case_study}

% -----------------------------------------------------------------
\section{\NerdEngine Processing Workflow:
         Step-by-Step Guide with Component~Contributions}
\label{sec:customnerd_workflow}


The end-to-end processing workflow in the \textbf{\NerdEngine} system is
a multi-stage pipeline designed to transform a user question into a
well-structured, citation-rich answer. Each stage contributes a distinct
function—validation, retrieval, filtering, summarisation, or~synthesis—
and together they deliver precise, evidence-backed~responses.

\begin{enumerate}

\item \textbf{\hl{User Submits Question} %MDPI: RESPONSE: Please leave as is.
}\\
      The user enters a question via the web interface, defining the
      research~scope.
      \begin{itemize}
        \item Front-end Interface captures input.
        \item Query Handler prepares it for validation.
      \end{itemize}

\item \textbf{\hl{Question Validation} %MDPI: RESPONSE: Please leave as is.
}\\
      The system checks that the query is suitable for academic research;
      irrelevant topics (e.g.,\ recipes, animals) trigger a friendly~error.
      \begin{itemize}
        \item \textbf{\hl{LLM} %MDPI: RESPONSE: Please leave as is.
} (\textit{\hl{GPT-4-turbo} %MDPI: RESPONSE: Please leave as is.
}) performs semantic
              validation.
        \item FastAPI server routes the result to the front-end.
      \end{itemize}

\item \textbf{\hl{Generate Search Queries} %MDPI: RESPONSE: Please leave as is.
}\\
      Five academic search strings are created—one general and four that
      explore different~angles.
      \begin{itemize}
        \item LLM Query Generator crafts optimised prompts.
        \item Helper functions log and prepare the queries.
        \item Session Tracker ties queries to the user session.
      \end{itemize}

\item \textbf{\hl{Collect Articles from APIs} %MDPI: RESPONSE: Please leave as is.
}\\
      Queries are dispatched to PubMed, Springer, Wiley, Elsevier, and~      Oxford Academic in~parallel.
      \begin{itemize}
        \item API Integrations layer handles requests.
        \item Concurrency Manager lowers latency.
        \item Deduplication logic removes repeats.
      \end{itemize}

\item \textbf{\hl{Article Processing} %MDPI: RESPONSE: Please leave as is.
}\\
      Titles, authors, journal, and~abstracts are~normalised.
      \begin{itemize}
        \item Metadata Extraction module parses entries.
        \item Backend Parser enforces uniform structure.
      \end{itemize}

\item \textbf{\hl{Relevance Classification} %MDPI: RESPONSE: Please leave as is.
}\\
      Each article is ranked for topical~fit.
      \begin{itemize}
        \item LLM Relevance Classifier scores alignment.
        \item Concurrent Evaluation engine processes batches.
        \item Filter Pipeline drops unrelated papers.
      \end{itemize}

\item \textbf{\hl{Full-Text Retrieval (if available)} %MDPI: RESPONSE: Please leave as is.
}\\
      Open-access versions are fetched; otherwise abstracts are~retained.
      \begin{itemize}
        \item Full-Text Retriever connects to repositories.
        \item Access-Control logic ensures compliance.
        \item Fallback mechanism guarantees usable text.
      \end{itemize}

\item \textbf{\hl{Generate Summary} %MDPI: RESPONSE: Please leave as is.
}\\
      Each relevant article is condensed into a domain-specific~summary.
      \begin{itemize}
        \item LLM Summariser writes clear digests.
        \item Summary Structurer links to metadata.
      \end{itemize}

\item \textbf{\hl{Collect All Summaries} %MDPI: RESPONSE: Please leave as is.
}\\
      Summaries are pooled into a single evidence~set.
      \begin{itemize}
        \item Summary Aggregator organises content.
        \item Session Tracker maintains integrity.
      \end{itemize}

\item \textbf{\hl{Synthesise Final Response} %MDPI: RESPONSE: Please leave as is.
}\\
      The evidence pool drives creation of a coherent, well-cited~answer.
      \begin{itemize}
        \item LLM Response Synthesiser composes the narrative.
        \item Tone Optimiser \& Guardrails enforce professionalism.
        \item Citation Embedder inserts inline references.
      \end{itemize}

\item \textbf{\hl{Extract Citations and Match to Articles} %MDPI: RESPONSE: Please leave as is.
}\\
      Inline citations are mapped to full~references.
      \begin{itemize}
        \item Citation-Matcher engine links references to sources.
        \item Formatter outputs MLA, APA, or~journal-specified style.
      \end{itemize}

\item \textbf{\hl{Format Final Output} %MDPI: RESPONSE: Please leave as is.
}\\
      The response text, citations, summaries, and~links are~packaged.
      \begin{itemize}
        \item Response Formatter assembles the payload.
        \item SSE Broadcaster streams updates to the UI.
      \end{itemize}

\item \textbf{\hl{Return to User} %MDPI: RESPONSE: Please leave as is.
}\\
      The user receives a comprehensive, academically valid answer with
      real-time progress~indicators.
      \begin{itemize}
        \item Front-end Renderer displays the response.
        \item Live UI Updater reflects pipeline status via SSE.
      \end{itemize}

\end{enumerate}

\paragraph{\textbf{Final Summary}}
\NerdEngine orchestrates validation, retrieval, filtering, summarisation,
and synthesis in a single, AI-assisted pipeline. Real-time updates, smart
fallbacks, and~deep integration with academic sources make it a fast,
reliable, and~richly informative tool for students, educators, and~researchers~alike.

\input{figures/custom_nerd_workflow}


% -----------------------------------------------------------------

% --------------------------------------------------------

% -----------------------------------------------------------------


\section{\NerdEngine Thin-Client~Architecture}
\label{sec:system_architecture_added}

\NerdEngine uses a thin-client architecture with a Python FastAPI backend and a
lightweight HTML/JavaScript frontend connected by a configuration layer
(saved states, prompts, environment keys). The~backend exposes a nine-stage
pipeline with Server-Sent Events (SSE) for live progress; the frontend streams
updates and renders intermediate artefacts and final answers~\cite{gao2024rag,lewis2020retrieval}. %\textbf{\textcolor{red}{TBD --- diagram for SSE pipeline workflow?}\textcolor{blue}{---done}}

{Figure~\ref{fig:website-architecture} in Section~\ref{sec:customnerd_case} shows the frontend--backend--storage data flow; SSE events communicate pipeline stage progress to the browser during retrieval and synthesis.}

\subsection{Backend (FastAPI, Python)}
Key modules include: routing and SSE broadcasting, API integration clients
(PubMed, GNews/NewsAPI/Guardian, arXiv/NASA ADS, Stack Overflow), the~LLM layer
(OpenAI, Gemini, Claude, or~Ollama), helper utilities (deduplication, rate limiting, retry/backoff),
and a similarity engine (TF–IDF, cosine similarity, Jaccard overlap, fuzzy
matching) for chat-history~recall.

\subsection{Frontend (HTML/JS)}
The UI provides configuration-driven panels (branding, prompts, environment,
user flow) and renders streamed updates via SSE. It can be adapted to React with
Tailwind CSS for larger deployments while preserving the same backend~contract.

\subsection{Saved States and~Reproducibility}
\textls[-35]{Each domain template under \texttt{saved\_states/} bundles \texttt{openai\_prompts.py},
\texttt{user\_env.js},} \texttt{user\_search\_apis.py}, \texttt{user\_list\_search.py},
\texttt{variables.env}, optional \texttt{clean\_query.py}, and~\texttt{historical\_answer.json}.
Loading a state deterministically restores behaviour across machines and~teams.

\subsection{Concurrency, Resilience, Observability}
External calls use async concurrency with exponential backoff, jitter, and~short
circuit suppression on repeated failures~\cite{nygard2018releaseit}
. SSE keeps the UI
responsive under variable provider latencies. Structured logs are kept per session
and per stage for audit and~replay.


\vspace{0.25em}
% \noindent\textit{Diagram suggestions:}
% \input{figures/architecture_overview}
% \input{figures/component_interactions}

% -----------------------------------------------------------------

% -----------------------------------------------------------------
\section{Implementation Walkthrough from \textbf{\texttt{main.py}} }
\label{sec:impl_from_main_added}
\unskip

\subsection{Endpoints}
\begin{itemize}
  \item \hl{\textbf{/health}:} %MDPI: RESPONSE: Please leave as is.
 Liveness probe.
  \item \textbf{/sse}: Opens an SSE stream keyed by \texttt{session\_id}; the server
        holds an \texttt{asyncio.Queue} per session and streams JSON updates.
  \item \hl{\textbf{/process\_detailed\_combined\_query}:} %MDPI: RESPONSE: Please leave as is.
 Primary entrypoint; accepts
        user query, toggles (database/PMIDs/PDF), optional files, and~schedules
        a background~task \\
        \texttt{process\_detailed\_combined\_logic}; returns a
        \texttt{session\_id} for streaming.
  \item \hl{\textbf{/fetch\_frontend\_config}, \textbf{/update\_frontend\_config}:} %MDPI: RESPONSE: Please leave as is.
 Read/write UI
        configuration (\texttt{user\_env.js}) and assets (logos).
  \item \hl{\textbf{/fetch\_env\_config}, \textbf{/update\_env\_config}:} %MDPI: RESPONSE: Please leave as is.
 Manage \texttt{variables.env}
        and hot-reload API keys/clients when possible.
  \item \hl{\textbf{/fetch\_prompts\_config}, \textbf{/update\_prompts\_config}:} %MDPI: RESPONSE: Please leave as is.
 Export/persist
        prompt templates (classification, relevance, summarisation, final response).
  \item \hl{\textbf{/fetch\_backend\_config}, \textbf{/update\_backend\_config}:} %MDPI: RESPONSE: Please leave as is.
 Read/write domain
        implementations for normal~search\\
    (\texttt{user\_search\_apis.py}), ID-specific
        search (\texttt{user\_list\_search.py}), and~optional \texttt{clean\_\allowbreak query.py}.
  \item \hl{\textbf{/save\_state}, \textbf{/list\_saved\_states}, \textbf{/load\_state}, \textbf{/delete\_state}:} %MDPI: RESPONSE: Please leave as is.

        Deterministic save/restore of domain bundles under \texttt{saved\_states/}.
  \item \hl{\textbf{/clear\_chat\_history}, \textbf{/history\_recent}, \textbf{/similar\_questions}:} %MDPI: RESPONSE: Please leave as is.

        Manage and query chat-history JSON with TF–IDF (word/char), Jaccard, and~fuzzy similarity.
\end{itemize}

\subsection{Background Task~Flow}
The task \texttt{process\_detailed\_combined\_logic} realises the~methodology:
\begin{enumerate}
  \item Generate a UUID \texttt{session\_id}; register an SSE queue.
  \item Run \texttt{query\_generation(input\_text)}; if \texttt{USER\_FLOW.query\_cleaning.visible}
        is true and \texttt{clean\_query.py} exists, refine the query list.
  \item Collect candidates concurrently via \texttt{collect\_articles(query\_list)}; respect
        provider rate limits; filter by \texttt{id\_list} if present.
  \item \textls[-25]{Process and normalise results via \texttt{concurrent\_organize\_database\_articles}; parse
        PDFs with \texttt{process\_pdf\_articles\_parallel}; enrich references via
        \texttt{process\_\allowbreak articles\_\allowbreak by\_\allowbreak url}.}
  \item Classify relevance via \texttt{concurrent\_relevance\_classification}; merge PDF items.
  \item Trim to token budgets via \texttt{trim\_relevant\_articles\_by\_token\_limit}.
  \item Synthesize final answer via \texttt{generate\_final\_response}; extract citations with
        \texttt{print\_\allowbreak referenced\_\allowbreak articles}.
  \item Stream intermediate updates and final JSON (\texttt{end\_output}, \texttt{citations\_obj}) over SSE.
  \item If chat history is enabled in \texttt{user\_env.js}, append the interaction to
        \texttt{historical\_\allowbreak answer.json} with timestamps.
\end{enumerate}

\subsection{Error Handling and~Resilience}
Provider errors trigger retries (exponential backoff with jitter) and quarantine
malformed items to avoid blocking the pipeline. SSE communicates degraded modes
(partial results) to the user. Horizontal scaling is feasible with multiple
FastAPI workers and stateless saved~states.

\subsection{Front-End~Implementation}
\label{app:frontend_implementation}
The client is a lightweight HTML/JS application with pages for main Q\&A, configuration, and~reference~drill-down.
\begin{enumerate}
  \item \hl{\textbf{API communication (\texttt{index.js}, \texttt{config.js}):}} %MDPI: RESPONSE: Please leave as is.
 Submits questions and strategy toggles, opens SSE streams, and~manages admin calls for prompts, environment, backend, and~frontend configuration endpoints.
  \item \hl{\textbf{Real-time updates (\texttt{EventSource}):}} %MDPI: RESPONSE: Please leave as is.
 Streams stage breadcrumbs and the final payload from \texttt{/sse?session\_id=...}, keeping the UI responsive under variable provider latencies.
  \item \hl{\textbf{Response formatting (\texttt{index.js/formatText}):}} %MDPI: RESPONSE: Please leave as is.
 Renders the generated answer and a reference section; persists artefacts in \texttt{localStorage} (e.g., \texttt{generatedAnswer}, \texttt{rawOutput}, \texttt{citations}).
  \item \hl{\textbf{PDF export (\texttt{index.js/generatePDF}):}} %MDPI: RESPONSE: Please leave as is.
 Exports the question and answer with a timestamp via \texttt{jsPDF}.
  \item \hl{\textbf{UI management:}} %MDPI: RESPONSE: Please leave as is.
 Handles strategy selection (PDF upload / identifier search / article search), similar-question suggestions (\texttt{/similar\_questions}), and~recent history (\texttt{/history\_recent}). Reference drill-down is provided via \texttt{reference.html} and \texttt{reference.js}. All toggleable features are controlled through the User Flow Configuration tab (see Appendix~\ref{app:web_interface_user_flow}).
\end{enumerate}

\subsection{Endpoint-to-Function~Mapping}
\label{app:endpoint_mapping}
Table~\ref{tab:endpoint_mapping} lists the primary REST and SSE endpoints exposed by the FastAPI backend and the internal functions they~invoke.

\begin{table}[H]

\caption{API Endpoint to Function~Mapping.}
\label{tab:endpoint_mapping}
\small
\renewcommand{\arraystretch}{1.2}
\begin{tabularx}{\textwidth}{
  >{\raggedright\arraybackslash}m{0.30\textwidth}
  >{\raggedright\arraybackslash}m{0.14\textwidth}
  X
}
\noalign{\hrule height 1pt}
\textbf{Endpoint} & \textbf{Purpose} & \textbf{Primary Function (Module)} \\
\midrule

\nolinkurl{GET /check_valid/{q}} & Validity gate & \texttt{determine\_question\_validity} (\nolinkurl{helper_functions}) \\

\nolinkurl{POST /process_detailed_combined_query} & Start pipeline & \texttt{process\_detailed\_combined\_logic} (\texttt{main}) \\

\nolinkurl{GET /sse} & Stream updates & \texttt{event\_generator}, \texttt{send\_update} (\texttt{main}) \\
\noalign{\hrule height 1pt}
\end{tabularx}
\normalsize
\end{table}

\begin{table}[H]\ContinuedFloat  

\caption{\emph{Cont.}}
\label{tab:endpoint_mapping}
\small
\renewcommand{\arraystretch}{1.2}
\begin{tabularx}{\textwidth}{
  >{\raggedright\arraybackslash}m{0.30\textwidth}
  >{\raggedright\arraybackslash}m{0.14\textwidth}
  X
}
\noalign{\hrule height 1pt}
\textbf{Endpoint} & \textbf{Purpose} & \textbf{Primary Function (Module)} \\
\midrule

\nolinkurl{GET /history_recent} & Recent history & Reads \nolinkurl{historical_answer.json}, sorts by timestamp (\texttt{main}) \\


\nolinkurl{GET /similar_questions} & Suggest similar & Hybrid similarity: TF--IDF, Jaccard, fuzzy; min-heap selection (\texttt{main}) \\


\nolinkurl{POST /clear_chat_history} & Clear history & Overwrites \nolinkurl{historical_answer.json} with empty array (\texttt{main}) \\


\nolinkurl{GET/POST /fetch_/update_frontend_config} & UI config & File I/O for \nolinkurl{user_env.js} (\texttt{main}) \\

%\nolinkurl{GET/POST /fetch_/update_env_config} & Env keys & File I/O and client re-initialization (\texttt{main}) \\
%
%\nolinkurl{GET/POST /fetch_/update_prompts_config} & Prompts & Constants I/O (\texttt{main}) \\
%
%\nolinkurl{GET/POST /fetch_/update_backend_config} & Search helpers & Write \nolinkurl{user_search_apis.py}, \nolinkurl{user_list_search.py}, \nolinkurl{clean_query.py} (\texttt{main}) \\
%
%\nolinkurl{POST /save_state} & Export bundle & Copy files to \nolinkurl{saved_states/} (\texttt{main}) \\
%
%\nolinkurl{GET /list_saved_states} & List bundles & Directory listing (\texttt{main}) \\
%
%\nolinkurl{POST /load_state} & Import bundle & Copy from \nolinkurl{saved_states/} (\texttt{main}) \\
%
%\nolinkurl{POST /delete_state} & Remove bundle & Directory delete (\texttt{main}) \\
%
%\nolinkurl{POST /generate_code_endpoint} & Code helper & \texttt{generate\_code\_from\_content} (\nolinkurl{helper_functions}) \\
%
%\nolinkurl{POST /generate_prompt_endpoint} & Prompt helper & \texttt{generate\_prompt\_from\_content} (\nolinkurl{helper_functions}) \\



%\nolinkurl{GET /check_valid/{q}} & Validity gate & \texttt{determine\_question\_validity} (\nolinkurl{helper_functions}) \\
%
%\nolinkurl{POST /process_detailed_combined_query} & Start pipeline & \texttt{process\_detailed\_combined\_logic} (\texttt{main}) \\
%
%\nolinkurl{GET /sse} & Stream updates & \texttt{event\_generator}, \texttt{send\_update} (\texttt{main}) \\
%
%\nolinkurl{GET /history_recent} & Recent history & Reads \nolinkurl{historical_answer.json}, sorts by timestamp (\texttt{main}) \\
%
%\nolinkurl{GET /similar_questions} & Suggest similar & Hybrid similarity: TF--IDF, Jaccard, fuzzy; min-heap selection (\texttt{main}) \\
%
%\nolinkurl{POST /clear_chat_history} & Clear history & Overwrites \nolinkurl{historical_answer.json} with empty array (\texttt{main}) \\
%
%\nolinkurl{GET/POST /fetch_/update_frontend_config} & UI config & File I/O for \nolinkurl{user_env.js} (\texttt{main}) \\

\nolinkurl{GET/POST /fetch_/update_env_config} & Env keys & File I/O and client re-initialization (\texttt{main}) \\

\nolinkurl{GET/POST /fetch_/update_prompts_config} & Prompts & Constants I/O (\texttt{main}) \\

\nolinkurl{GET/POST /fetch_/update_backend_config} & Search helpers & Write \nolinkurl{user_search_apis.py}, \nolinkurl{user_list_search.py}, \nolinkurl{clean_query.py} (\texttt{main}) \\

\nolinkurl{POST /save_state} & Export bundle & Copy files to \nolinkurl{saved_states/} (\texttt{main}) \\

\nolinkurl{GET /list_saved_states} & List bundles & Directory listing (\texttt{main}) \\

\nolinkurl{POST /load_state} & Import bundle & Copy from \nolinkurl{saved_states/} (\texttt{main}) \\

\nolinkurl{POST /delete_state} & Remove bundle & Directory delete (\texttt{main}) \\

\nolinkurl{POST /generate_code_endpoint} & Code helper & \texttt{generate\_code\_from\_content} (\nolinkurl{helper_functions}) \\

\nolinkurl{POST /generate_prompt_endpoint} & Prompt helper & \texttt{generate\_prompt\_from\_content} (\nolinkurl{helper_functions}) \\

\noalign{\hrule height 1pt}
\end{tabularx}
\normalsize
\end{table}

\vspace{-6pt}
% \input{figures/impl_sequence_diagram}

% -----------------------------------------------------------------
\section{Technical Documentation: Backend and~Frontend  }
\label{sec:tech_docs_added}
\unskip

\subsection{Backend}
Routing, SSE (\texttt{/sse}), background tasks, provider wrappers, and~utilities
compose the service. Sessions are scoped by \texttt{session\_id}. Security guardrails
include environment-managed keys, production CORS, and~recommended API gateways.
New providers implement \texttt{collect\_articles} and plug into prompts and~UI.

\subsection{Frontend}
Baseline HTML/JS streams SSE updates. An~optional React/Tailwind implementation
can manage state (context/Redux), reconnect SSE hooks, and~custom renderers for
citations and code blocks while preserving the API~contract.

\subsection{Saved States and~Models}
Saved states checkpoint prompts, UI, and~environment keys. Model selection
(OpenAI, Gemini, Claude, or~Ollama) is parameterised; restoring a state plus
model/version pins reproducibility within model-drift bounds.  For~Ollama
deployments the saved state also records the \texttt{OLLAMA\_MODEL} and
\texttt{OLLAMA\_BASE\_URL} variables so that the exact local model can be
restored on any~machine.

\subsection{Migrating Between LLM~Providers}
\label{app:llm_migration}
\NerdEngine supports four LLM backends---OpenAI, Gemini, Claude, and Ollama---selectable via the \texttt{LLM} variable in \texttt{variables.env}.
When switching from one provider to another, the~following structured approach
ensures consistency and optimal performance:

\begin{enumerate}
    \item \textbf{\hl{Select the Closest Equivalent Model.} %MDPI: RESPONSE: Please leave as is.
}
    Identify the model from the new provider that most closely matches the capabilities and intended use case of the previous model (e.g., Gemini Pro as a replacement for GPT-4 for general-purpose tasks; or \texttt{llama3.1:8b} as a capable local replacement when moving to Ollama).

    \item \textbf{\hl{Test Existing Prompts.} %MDPI: RESPONSE: Please leave as is.
}
    Input the original prompts into the new model without modification. Compare outputs for tone, clarity, and~the absence of hallucinations or~inconsistencies.

    \item \textbf{\hl{Evaluate Performance.} %MDPI: RESPONSE: Please leave as is.
}
    If outputs are satisfactory, proceed with deployment. If~hallucinations are present, continue to Step~4.

    \item \textbf{\hl{Adjust Prompts for the New Model.} %MDPI: RESPONSE: Please leave as is.
}
    Research how the new model handles prompt structure, including syntax preferences, formatting cues (e.g., bold vs. caps for emphasis), and~tolerance for verbosity. Tailor prompts accordingly. For~example, Gemini responds better to concise instructions and markdown-style formatting; smaller Ollama models (e.g., \texttt{phi3:mini}, \texttt{llama3.2}) benefit from shorter, more directive prompts with explicit output format~constraints.

    \item \textbf{\hl{Tune Generation Parameters.} %MDPI: RESPONSE: Please leave as is.
}
    If prompt refinements do not resolve issues, lower temperature and top-$p$ values to reduce randomness and increase determinism, then re-test outputs against prior~benchmarks.

    \item \textbf{\hl{Iterate and Lock.} %MDPI: RESPONSE: Please leave as is.
}
    Repeat Steps 3--5 until output quality is satisfactory and consistent. Once acceptable prompts and settings are found, document them in the saved state for reproducibility.
\end{enumerate}

\subsubsection*{Ollama: Local LLM Deployment}
\label{app:ollama_local}
Selecting \texttt{LLM=Ollama} routes all inference through a locally hosted
Ollama server rather than a cloud API. This eliminates per-token billing and
data-egress privacy concerns at the cost of requiring local~compute.

\NerdEngine automates the entire local setup from the web interface: when
\texttt{LLM=Ollama} is saved and a model is selected, the~backend
(\texttt{/ollama\_setup} endpoint) sequentially checks for an existing Ollama
installation, installs it if absent (Homebrew on macOS; \texttt{curl} install
script on Linux), starts the \texttt{ollama serve} daemon, and~executes
\texttt{ollama pull <model>}---streaming live progress back to the browser
via Server-Sent Events. A~pre-flight status check (\texttt{/ollama\_status})
runs immediately when the Ollama tab is opened, reporting whether Ollama is
installed, whether the server is running, and~whether the selected model has
already been downloaded; this avoids redundant setup~steps.

Table~\ref{tab:ollama_models_appendix} lists the supported Ollama models grouped by
category.

\begin{table}[H]

\caption{Supported Ollama models in \NerdEngine (appendix reference).}
\label{tab:ollama_models_appendix}
\small
\renewcommand{\arraystretch}{1.3}
\begin{tabularx}{\textwidth}{lcccX}
\hline
\textbf{Model} & \textbf{Params} & \textbf{Disk} & \textbf{Min RAM} & \textbf{Notes} \\
\hline
\multicolumn{5}{l}{\textit{\hl{Recommended} %MDPI: Please confirm if the italics are necessary; if not, please remove them. The following highlights are the same. RESPONSE: Please leave as is (italics denote the model-category sub-headings and are intentional).
}} \\
\hline
\texttt{llama3.2}      & 3.2B  & $\sim$2\,GB   & 4\,GB  & Default; best balance of speed and~quality \\
\texttt{llama3.1:8b}   & 8B    & $\sim$4.7\,GB & 8\,GB  & Better reasoning; 128K~context \\
\hline
\multicolumn{5}{l}{\textit{\hl{Small / Low-Resource} %MDPI: RESPONSE: Please leave as is.
}} \\
\hline
\texttt{llama3.2:1b}       & 1B    & $\sim$1.3\,GB & 3\,GB  & Fastest; minimal~RAM \\
\texttt{qwen3:1.7b}        & 1.7B  & $\sim$1.1\,GB & 3\,GB  & Alibaba Qwen, very~compact \\
\texttt{deepseek-r1:1.5b}  & 1.5B  & $\sim$1.1\,GB & 3\,GB  & Reasoning on low-end~hardware \\
\texttt{phi3:mini}         & 3.8B  & $\sim$2.3\,GB & 4\,GB  & Microsoft Phi-3; punches above~weight \\
\texttt{gemma3:4b}         & 4B    & $\sim$2.5\,GB & 4\,GB  & Google Gemma 3~compact \\
\hline
\end{tabularx}
\end{table}


\begin{table}[H]\ContinuedFloat  

\caption{\emph{Cont.}}
\label{tab:ollama_models_appendix}
\small
\renewcommand{\arraystretch}{1.3}
\begin{tabularx}{\textwidth}{lcccX}
\hline
\textbf{Model} & \textbf{Params} & \textbf{Disk} & \textbf{Min RAM} & \textbf{Notes} \\
\hline
\multicolumn{5}{l}{\textit{\hl{High Quality (8\,GB+ RAM recommended)} %MDPI: RESPONSE: Please leave as is.
}} \\
\hline
\texttt{qwen3:8b}          & 8B    & $\sim$5\,GB   & 8\,GB  & Best reasoning at 8B~class \\
\texttt{mistral}           & 7B    & $\sim$4.1\,GB & 8\,GB  & Versatile general~purpose \\
\texttt{gemma2:9b}         & 9B    & $\sim$5.4\,GB & 8\,GB  & Google Gemma~2 \\
\texttt{gemma3:12b}        & 12B   & $\sim$7.7\,GB & 16\,GB & Google Gemma 3~flagship \\
\texttt{mistral-nemo:12b}  & 12B   & $\sim$7\,GB   & 16\,GB & Extended 128K~context \\
%\texttt{phi4}              & 14B   & $\sim$8.9\,GB & 16\,GB & Microsoft Phi-4; top-tier~quality \\
%\hline
%\multicolumn{5}{l}{\textit{\hl{Reasoning} %MDPI: RESPONSE: Please leave as is.
%}} \\
%\hline
%\texttt{deepseek-r1:7b}   & 7B    & $\sim$4.7\,GB & 8\,GB  & Chain-of-thought~reasoning \\
%\texttt{deepseek-r1:14b}  & 14B   & $\sim$8.9\,GB & 16\,GB & Deep multi-step~reasoning \\
%\hline
%\multicolumn{5}{l}{\textit{\hl{Code} %MDPI: RESPONSE: Please leave as is.
%}} \\
%\hline
%\texttt{codellama:7b}         & 7B    & $\sim$3.8\,GB & 8\,GB  & Meta Code~Llama \\
%\texttt{deepseek-coder:6.7b}  & 6.7B  & $\sim$3.8\,GB & 8\,GB  & DeepSeek~Coder \\
%\texttt{qwen2.5-coder:7b}     & 7B    & $\sim$4.4\,GB & 8\,GB  & Best coding model in~list \\
%\hline
%\multicolumn{5}{l}{\textit{\hl{Cloud / Hosted via Ollama} %MDPI: RESPONSE: Please leave as is.
%}} \\
%\hline
%\texttt{kimi-k2.5:cloud}  & Cloud & None          & None   & Moonshot Kimi K2.5; no local~download \\

%\multicolumn{5}{l}{\textit{\hl{Recommended} %MDPI: Please confirm if the italics are necessary; if not, please remove them. The following highlights are the same.
%}} \\
%\hline
%\texttt{llama3.2}      & 3.2B  & $\sim$2\,GB   & 4\,GB  & Default; best balance of speed and~quality \\
%\texttt{llama3.1:8b}   & 8B    & $\sim$4.7\,GB & 8\,GB  & Better reasoning; 128K~context \\
%\hline
%\multicolumn{5}{l}{\textit{\hl{Small / Low-Resource} %MDPI: RESPONSE: Please leave as is.
%}} \\
%\hline
%\texttt{llama3.2:1b}       & 1B    & $\sim$1.3\,GB & 3\,GB  & Fastest; minimal~RAM \\
%\texttt{qwen3:1.7b}        & 1.7B  & $\sim$1.1\,GB & 3\,GB  & Alibaba Qwen, very~compact \\
%\texttt{deepseek-r1:1.5b}  & 1.5B  & $\sim$1.1\,GB & 3\,GB  & Reasoning on low-end~hardware \\
%\texttt{phi3:mini}         & 3.8B  & $\sim$2.3\,GB & 4\,GB  & Microsoft Phi-3; punches above~weight \\
%\texttt{gemma3:4b}         & 4B    & $\sim$2.5\,GB & 4\,GB  & Google Gemma 3~compact \\
%\hline
\multicolumn{5}{l}{\textit{\hl{High Quality (8\,GB+ RAM recommended)} %MDPI: RESPONSE: Please leave as is.
}} \\
\hline
%\texttt{qwen3:8b}          & 8B    & $\sim$5\,GB   & 8\,GB  & Best reasoning at 8B~class \\
%\texttt{mistral}           & 7B    & $\sim$4.1\,GB & 8\,GB  & Versatile general~purpose \\
%\texttt{gemma2:9b}         & 9B    & $\sim$5.4\,GB & 8\,GB  & Google Gemma~2 \\
%\texttt{gemma3:12b}        & 12B   & $\sim$7.7\,GB & 16\,GB & Google Gemma 3~flagship \\
%\texttt{mistral-nemo:12b}  & 12B   & $\sim$7\,GB   & 16\,GB & Extended 128K~context \\
\texttt{phi4}              & 14B   & $\sim$8.9\,GB & 16\,GB & Microsoft Phi-4; top-tier~quality \\
\hline
\multicolumn{5}{l}{\textit{\hl{Reasoning} %MDPI: RESPONSE: Please leave as is.
}} \\
\hline
\texttt{deepseek-r1:7b}   & 7B    & $\sim$4.7\,GB & 8\,GB  & Chain-of-thought~reasoning \\
\texttt{deepseek-r1:14b}  & 14B   & $\sim$8.9\,GB & 16\,GB & Deep multi-step~reasoning \\
\hline
\multicolumn{5}{l}{\textit{\hl{Code} %MDPI: RESPONSE: Please leave as is.
}} \\
\hline
\texttt{codellama:7b}         & 7B    & $\sim$3.8\,GB & 8\,GB  & Meta Code~Llama \\
\texttt{deepseek-coder:6.7b}  & 6.7B  & $\sim$3.8\,GB & 8\,GB  & DeepSeek~Coder \\
\texttt{qwen2.5-coder:7b}     & 7B    & $\sim$4.4\,GB & 8\,GB  & Best coding model in~list \\
\hline
\multicolumn{5}{l}{\textit{\hl{Cloud / Hosted via Ollama} %MDPI: RESPONSE: Please leave as is.
}} \\
\hline
\texttt{kimi-k2.5:cloud}  & Cloud & None          & None   & Moonshot Kimi K2.5; no local~download \\
\hline
\end{tabularx}
\end{table}

% \input{figures/state_mapping}

% -----------------------------------------------------------------
% \section{Case Study Expansion (Added)}
% \label{sec:case_study_expansion_added}

% \textcolor{red}{TBD: Does this section make Appendix \ref{prompt-templates} redundant. Please merge and eliminate one of these}

% We extend Section~\ref{sec:customnerd_workflow} by detailing domain pipelines:
% \begin{itemize}[leftmargin=1.5em]
%   \item \textbf{DietNerd}: PubMed via Bio.Entrez; peer-review and human-study filters; formal synthesis with AMA/APA references, dosage/risk notes; UI reference panels.
%   \item \textbf{NewsNerd}: GNews/NewsAPI/Guardian; aggressive deduplication and recency; concise summaries with inline links; low latency prioritised.
%   \item \textbf{CloudNerd}: Stack Exchange API integration (\texttt{user\_search\_apis.py}) with two-stage retrieval (search questions by relevance, fetch top answers by votes); HTML cleaning preserves code blocks; relevance classification filters for cloud technology topics (AWS, Azure, GCP, Kubernetes, DevOps, IaC, serverless, networking, security); \texttt{ABSTRACT\_EXTRACTION\_PROMPT} extracts technical details (code snippets, configurations, error messages, performance metrics, security considerations); \texttt{FINAL\_RESPONSE\_PROMPT} synthesises 8--20 posts with security warnings, cost implications, and Stack Overflow citations; query expansion via \texttt{GENERAL\_QUERY\_PROMPT} generates 8--12 JSON-formatted variations; optional \texttt{clean\_query.py} parses JSON and extracts expanded queries; rate limiting (300/day without key, 10,000/day with \texttt{STACK\_API\_KEY}); cloud-neutral UI with preserved code blocks.
%   \item \textbf{SpaceNerd}: arXiv, NASA Images, optional ADS; identifier integrity (arXiv IDs, ADS bibcodes); optional query cleaner; balanced technical/accessible prose.
% \end{itemize}

% \noindent\emph{These case studies are representative but not exhaustive — the \NerdEngine framework can be extended to additional domains such as finance, law, education, and policy.}

% \subsubsection{\NerdEngine Workflow Example: ``How to secure a Kubernetes cluster?''}

% To illustrate \NerdEngine's end-to-end processing, we trace a concrete query through the pipeline:

% \textbf{Step 1: Question Validation}
% The user submits ``How to secure a Kubernetes cluster?'' The \texttt{DETERMINE\_QUESTION\_VALIDITY\_PROMPT} classifies this as cloud technology (Kubernetes, security) and returns ``True''.

% \textbf{Step 2: Query Generation}
% \texttt{GENERAL\_QUERY\_PROMPT} expands the question into 12 JSON-formatted variations:
% \begin{lstlisting}[language=json, basicstyle=\small]
% {
%   "expanded_queries": [
%     "how to secure kubernetes cluster",
%     "kubernetes cluster security best practices",
%     "protecting kubernetes from attacks",
%     "kubernetes role based access control setup",
%     "kubernetes pod security policies",
%     "network security in kubernetes",
%     "tls certificates in kubernetes cluster",
%     "kubernetes security hardening",
%     "securing kubernetes api server",
%     "kubernetes authentication and authorization",
%     "kubernetes secrets management best practices",
%     "how to monitor kubernetes security"
%   ]
% }
% \end{lstlisting}
% If \texttt{USER\_FLOW.query\_cleaning.visible} is enabled, \texttt{clean\_query.py} parses this JSON and extracts the \texttt{expanded\_queries} array.

% \textbf{Step 3: Article Collection}
% \texttt{user\_search\_apis.collect\_articles} executes the two-stage Stack Exchange API process:
% \begin{enumerate}[leftmargin=1.5em]
%   \item For each query, search Stack Overflow questions by relevance (\texttt{sort=relevance}).
%   \item For each question, fetch top answers sorted by votes (\texttt{sort=votes}), up to 5 answers per question.
% \end{enumerate}
% The function deduplicates by \texttt{question\_id}, prioritises accepted answers (\texttt{is\_accepted=True}), and collects up to 50 total answers. HTML cleaning via BeautifulSoup preserves code blocks in markdown format (e.g., \texttt{\textbackslash n```\textbackslash n\{code\}\textbackslash n```\textbackslash n}).

% \textbf{Step 4: Relevance Classification}
% \texttt{concurrent\_relevance\_classification} applies \texttt{RELEVANCE\_CLASSIFIER\_PROMPT} to each post. For example:
% \begin{itemize}[leftmargin=1.5em]
%   \item Post: ``Enabling RBAC and using network policies are essential steps. Misconfigured permissions often lead to breaches.'' → \texttt{yes} (relevant + security concern).
%   \item Post: ``Here's a guide on building React apps with Webpack for frontend deployment.'' → \texttt{no} (unrelated technology).
% \end{itemize}

% \textbf{Step 5: Article Typing and Extraction}
% \texttt{ARTICLE\_TYPE\_PROMPT} distinguishes questions from answers. \texttt{ABSTRACT\_EXTRACTION\_PROMPT} extracts structured information:
% \begin{itemize}[leftmargin=1.5em]
%   \item \textbf{Problem \& Context:} User wants to secure a Kubernetes cluster; environment: Kubernetes v1.28, AWS EKS.
%   \item \textbf{Proposed Solutions:} Enable RBAC, implement Network Policies, enable audit logs, use TLS certificates.
%   \item \textbf{Best Answer:} Step-by-step guide: (1) Create RBAC roles with least privilege, (2) Apply Network Policies to restrict pod-to-pod communication, (3) Enable audit logging, (4) Configure TLS for API server.
%   \item \textbf{Risks \& Limitations:} Misconfigured RBAC can expose sensitive workloads; network policies can block legitimate traffic if not tested.
%   \item \textbf{Security Considerations:} IAM permissions, encryption at rest (etcd), RBAC, firewall rules, compliance (CIS benchmarks).
%   \item \textbf{Tools/Frameworks:} kubectl, Falco, kube-bench, OpenID Connect (OIDC).
%   \item \textbf{Community Signals:} Accepted answer, 247 upvotes, author reputation 15,432.
% \end{itemize}

% \textbf{Step 6: Final Synthesis}
% \texttt{generate\_final\_response} uses \texttt{FINAL\_RESPONSE\_PROMPT} to synthesise 8--20 posts (preferring more when available). The prompt prioritises:
% \begin{itemize}[leftmargin=1.5em]
%   \item Accepted answers and highly upvoted responses.
%   \item Answers with reproducible code/configs and official documentation references.
%   \item Security warnings and cost implications.
%   \item Multi-provider coverage (AWS EKS, Azure AKS, GCP GKE, on-premises).
% \end{itemize}
% The output includes structured sections: Access Control \& Identity Management, Network Policies \& Encryption, Audit \& Monitoring, Risks \& Trade-offs, and Conclusion. Each section cites specific Stack Overflow posts with inline references \texttt{[1]}, \texttt{[2]}, etc.

% \textbf{Step 7: Citation Extraction}
% \texttt{print\_referenced\_articles} maps inline references to full citations:
% \begin{itemize}[leftmargin=1.5em]
%   \item \texttt{[1]} Stack Overflow. ``How to use Kubernetes RBAC to secure access?'' \texttt{https://stackoverflow.com/q/xxxxxx}
%   \item \texttt{[2]} Stack Overflow. ``Best practices for RBAC in Kubernetes clusters.'' \texttt{https://stackoverflow.com/q/yyyyyy}
% \end{itemize}
% The final payload includes \texttt{end\_output} (formatted answer), \texttt{citations\_obj} (structured references), and \texttt{DISCLAIMER\_TEXT} (educational disclaimer about consulting certified cloud professionals).

% \textbf{Performance Notes:}
% \begin{itemize}[leftmargin=1.5em]
%   \item Stack Exchange API rate limits: 300 requests/day without key, 10,000/day with \texttt{STACK\_API\_KEY}.
%   \item Implementation includes 1-second delays between requests and error handling for 429 (rate limit) responses.
%   \item HTML parsing adds processing overhead but preserves code readability for technical accuracy.
% \end{itemize}

% -----------------------------------------------------------------
\section{Implementation Workflow: From Zero to Domain in~Hours}
\label{app:implementation_workflow}

To implement a new domain (e.g., ``LegalNerd'' for legal research), domain specialists follow a structured workflow that typically requires 2--3 h. Table~\ref{tab:implementation_workflow} presents the step-by-step process with associated time~estimates.

\begin{table}[H]

\caption{Domain Implementation~Workflow}
\label{tab:implementation_workflow}
\small
\renewcommand{\arraystretch}{1.3}
\begin{tabularx}{\textwidth}{m{2.5cm}m{1.5cm}X}
\hline
\textbf{Step} & \textbf{Time} & \textbf{Activities} \\
\hline
\textbf{\hl{Frontend Configuration} %MDPI: RESPONSE: Please leave as is.
} & 
15 min & 
Set site name, upload logo, choose icon, write tagline, customize colors and fonts, compose a domain-specific~disclaimer \\
\hline
\textbf{\hl{Environment Setup} %MDPI: RESPONSE: Please leave as is.
} & 
5 min & 
Add required API keys (e.g., Westlaw, LexisNexis), select an AI provider (OpenAI, Gemini, Claude, or~Ollama for fully local inference) \\
\hline
\textbf{\hl{Search Integration} %MDPI: RESPONSE: Please leave as is.
} & 
30--60 min & 
Configure the primary search function (custom code or AI-assisted generator), optionally enable identifier-based search, and~configure query refinement for citation~normalization \\
\hline
\textbf{\hl{AI Prompt Customization} %MDPI: RESPONSE: Please leave as is.
} & 
60--90 min & 
Customize prompts for question validation, query generation, relevance classification (e.g., precedential value, jurisdiction), article processing (case facts, holdings, reasoning), and~response synthesis (Bluebook citations and legal disclaimers). An~AI-assisted prompt generator can be used to accelerate this~step \\
\hline
\textbf{\hl{User Flow Configuration} %MDPI: RESPONSE: Please leave as is.
} & 
10 min & 
Customize search strategy labels and tooltips, configure visibility toggles (including \emph{\hl{Deep Search} %MDPI: RESPONSE: Please leave as is.
}/\texttt{tiered\_collect\_articles}), and~enable or disable query cleaning, references, and~chat history; inline SVG diagrams explain each~toggle \\
\hline
\textbf{\hl{State Saving} %MDPI: RESPONSE: Please leave as is.
} & 
2 min & 
Save the complete configuration as a named state for reproducible deployment, team sharing, and~future~restoration \\
\hline
\textbf{\hl{Total} %MDPI: RESPONSE: Please leave as is.
} & 
2--3 h & 
Fully functional, production-ready domain~implementation \\
\hline
\end{tabularx}
\normalsize
\end{table}

This demonstrates that fully functional, production-ready domain implementations can be created in hours rather than weeks, without~requiring software engineering expertise or modifications to the core~system.

\section{CloudNerd: Multi-Source Retrieval and Query~Refinement}
\label{app:retrieval_pipeline}

This appendix provides implementation details for CloudNerd retrieval: the baseline configuration emphasizes Stack Overflow via the Stack Exchange API; the \texttt{CloudNerd Advanced} saved state extends the same \texttt{collect\_articles} contract with AWS, Azure, and~CloudFest RSS (plus related search and resource links) and parallel HTML text extraction. Excerpts below reflect the Advanced module; the code in the repository is executed at runtime and is included for reproducibility and~auditability.

\subsection{CloudNerd Retrieval Configuration (120-Question Benchmark)}
\label{app:cloudnerd_retrieval_config}

Benchmark evaluation uses CloudNerd rather than the Advanced RSS bundle: multi-plane tiered Stack Overflow retrieval (\texttt{tiered\_collect\_articles}), citation recovery, supplemental gap-fill, and~the evidence-constrained synthesis prompt. Pipeline architecture, retrieval profiles, and~User Flow flags are documented in Appendix~\ref{app:cloudnerd_eval_pipeline}. The~Advanced \texttt{collect\_articles} implementation below remains the reference for multi-source saved-state~deployments.

\subsection{CloudNerd Configuration~Walkthrough}
\label{app:cloudnerd_config}

This subsection documents the CloudNerd-specific configuration choices made through the \NerdEngine web interface. It complements the generic interface descriptions in Appendix~\ref{app:web_interface} and the prompt texts in Appendix~\ref{prompt-templates}.

\subsubsection{Frontend Visual Identity}

The visual identity was configured through the Frontend Configuration Tab (see Appendix~\ref{app:web_interface_frontend}) to reflect cloud computing aesthetics while maintaining professional clarity:

\textbf{\hl{Branding Elements:} %MDPI: RESPONSE: Please leave as is.
}
\begin{itemize}
    \item \textls[-15]{\textbf{\hl{Site Name:} %MDPI: RESPONSE: Please leave as is.
} ``CloudExpert Nerd''---chosen to emphasize expertise in cloud technologies}
    \item \textbf{\hl{Site Icon:} %MDPI: RESPONSE: Please leave as is.
} cloud emoji (Unicode U+2601)---Cloud Emoji---provides instant visual recognition of the domain focus
    \item \textbf{\hl{Site Tagline:} %MDPI: RESPONSE: Please leave as is.
} ``We answer your cloud-related questions based on Stack Overflow'' (baseline); the Advanced retrieval bundle additionally ingests vendor blogs and industry feeds while preserving the same UI and pipeline shape
    \item \textbf{\hl{Question Placeholder:} %MDPI: RESPONSE: Please leave as is.
} ``Enter your cloud-related question here\dots.''---guides users toward appropriate query formulation
\end{itemize}

\textbf{\hl{Visual Styling:} %MDPI: RESPONSE: Please leave as is.
}
\begin{itemize}
    \item \textbf{\hl{Background Color:} %MDPI: RESPONSE: Please leave as is.
} \texttt{\#fafafa} (light grey)---neutral backdrop that reduces eye strain during extended technical reading
    \item \textbf{\hl{Font Family:} %MDPI: RESPONSE: Please leave as is.
} \texttt{'Rubik', sans-serif}---modern, readable sans-serif optimized for technical content
    \item \textbf{\hl{Submit Button:} %MDPI: RESPONSE: Please leave as is.
} \texttt{\#007bff} (standard blue)---recognizable action color consistent with cloud provider interfaces
\end{itemize}

\textbf{\hl{Disclaimer Text:} %MDPI: RESPONSE: Please leave as is.
} A comprehensive educational disclaimer was configured emphasizing that ``Everything on this website is for educational and informational purposes only. The~content is based on publicly available information, including community discussions and third-party sources, and~may not always reflect the most current practices or official guidance. It is not intended to replace professional consultation, official cloud provider documentation, or~vendor-specific~support.''

\subsubsection{Backend Prompt Configuration Details}

All five prompt categories were customized for technical troubleshooting. The~full prompt texts are provided in Appendix~\ref{prompt-templates}; this section documents the configuration choices for each~category.

\textbf{\hl{1. Question Validation Prompt:} %MDPI: RESPONSE: Please leave as is.
}

The \texttt{DETERMINE\_QUESTION\_VALIDITY\_PROMPT} was configured with comprehensive classification rules to identify cloud-relevant queries while filtering cross-domain contamination. The~prompt recognizes cloud technology topics~including:
\begin{itemize}
    \item Cloud computing platforms: AWS, Azure, GCP, OCI, DigitalOcean
    \item Cloud services: S3, EC2, Lambda, Azure Functions, GKE, Pub/Sub
    \item Infrastructure-as-code: Terraform, Pulumi, ARM, CloudFormation
    \item Container technologies: Kubernetes, Docker, container orchestration
    \item DevOps practices: CI/CD pipelines, observability, monitoring, scaling, serverless
    \item Cloud networking: VPC, load balancers, DNS, CDN, firewalls
    \item Security and compliance: IAM, multi-cloud strategies, architecture decisions
\end{itemize}

The prompt implements granular rejection categories to maintain domain boundaries: \texttt{"False \hl{-} %MDPI: Please check if hyphen should be revised to emdash. RESPONSE: No --- the hyphen is part of the literal code string "False - ..." emitted by the classifier and must remain a hyphen, not an em-dash.
 General Tech"} for non-cloud programming questions (frontend coding, mobile apps, gaming), \texttt{"False \hl{-} Personal"} for lifestyle queries, \texttt{"False \hl{-} Health"} for medical questions, \texttt{"False \hl{-} Recipe"} for cooking, \texttt{"False \hl{-} Animal"} for pet advice, and~\texttt{"False \hl{-} Other"} for unclassifiable topics. This multi-category approach prevents the system from attempting to answer questions better suited for other \NerdEngine domains. Appendix~\ref{app:cloudnerd_question_validity} contains the prompt~text.

\textbf{\hl{2. Query Generation and Expansion:} %MDPI: RESPONSE: Please leave as is.
}

CloudNerd's \texttt{GENERAL\_QUERY\_PROMPT} generates JSON-formatted query lists rather than Boolean search strings. The~prompt is instructed to create 8--12 variations combining exact rephrases, synonyms, provider-specific terminology, and~service-specific~versions.

For example, given the user question ``How to secure a Kubernetes cluster?'', the~system generates:

\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
{
  "expanded_queries": [
    "how to secure kubernetes cluster",
    "kubernetes cluster security best practices",
    "protecting kubernetes from attacks",
    "kubernetes role based access control setup",
    "kubernetes pod security policies",
    "network security in kubernetes",
    "tls certificates in kubernetes cluster",
    "kubernetes security hardening",
    "securing kubernetes api server",
    "kubernetes authentication and authorization",
    "kubernetes secrets management best practices",
    "how to monitor kubernetes security"
  ]
}
\end{lstlisting}
\vspace{6pt}

These variations systematically explore different security dimensions (RBAC, network policies, encryption, and monitoring) without requiring users to understand the technical breakdown. The~JSON format enables programmatic extraction via the query refinement pipeline. Appendix~\ref{app:cloudnerd_query_generation} contains the prompt~text.

\textbf{\hl{3. Relevance Classification:} %MDPI: RESPONSE: Please leave as is.
}

The \texttt{RELEVANCE\_CLASSIFIER\_PROMPT} was configured to evaluate Stack Overflow posts along two dimensions: technical utility and security value. The~prompt instructs the LLM to return \texttt{"yes"} if a post contains relevant technical information to answer the query or mentions important safety, security, or~reliability concerns. Posts focusing on unrelated technologies (frontend UI frameworks, desktop applications, gaming engines) are rejected with \texttt{"no"}.

Example~behavior:
\begin{itemize}
    \item \textbf{\hl{Query} %MDPI: RESPONSE: Please leave as is.
:} ``How to configure IAM roles in AWS Lambda?''
    
    \textbf{\hl{Post:} %MDPI: RESPONSE: Please leave as is.
} ``You can assign an IAM role to a Lambda function by setting its execution role in the AWS console or using CloudFormation~templates.''
    
    \textbf{\hl{Classification:} %MDPI: RESPONSE: Please leave as is.
} \texttt{yes} (directly addresses the query with actionable steps)
    
    \item \textbf{\hl{Query:} %MDPI: RESPONSE: Please leave as is.
} ``What are best practices for securing Kubernetes clusters?''
    
    \textbf{\hl{Post:} %MDPI: RESPONSE: Please leave as is.
} ``Enabling RBAC and using network policies are essential steps. Misconfigured permissions often lead to~breaches.''
    
    \textbf{\hl{Classification:} %MDPI: RESPONSE: Please leave as is.
} \texttt{yes} (provides best practices and security warnings)
    
    \item \textbf{\hl{Query:} %MDPI: RESPONSE: Please leave as is.
} ``How to set up CI/CD for serverless apps?''
    
    \textbf{\hl{Post:} %MDPI: RESPONSE: Please leave as is.
} ``Here's a guide on building React apps with Webpack for frontend~deployment.''
    
    \textbf{\hl{Classification:} %MDPI: RESPONSE: Please leave as is.
} \texttt{no} (discusses frontend tooling, not cloud CI/CD)
\end{itemize}

Appendix~\ref{app:cloudnerd_relevance} contains the prompt~text.

\textbf{\hl{4. Article Processing and Information Extraction:} %MDPI: RESPONSE: Please leave as is.
}

CloudNerd uses two specialized prompts for processing retrieved evidence (Stack Overflow Q\&A and, in~Advanced, vendor or industry blog text shaped like answers):

\textit{\hl{Article Type Classification:} %MDPI: RESPONSE: Please leave as is.
} The \texttt{ARTICLE\_TYPE\_PROMPT} distinguishes between questions (problem statements, requests for help) and answers (solutions, explanations, troubleshooting steps). This classification enables differential processing: questions provide context about common problems, while answers contain actionable~solutions.

\textit{\hl{Structured Information Extraction:} %MDPI: RESPONSE: Please leave as is.
} The \texttt{ABSTRACT\_EXTRACTION\_PROMPT} was configured to extract 13 categories of technical information from Stack Overflow posts and analogous bodies from feed-derived~pages:
\begin{enumerate}
    \item \textbf{\hl{Problem \& Context:} %MDPI: RESPONSE: Please leave as is.
} Environment details, cloud provider, service names, versions, error messages
    \item \textbf{\hl{Proposed Solutions:} %MDPI: RESPONSE: Please leave as is.
} All approaches discussed in the answers
    \item \textbf{\hl{Best Answer / Accepted Fix:} %MDPI: RESPONSE: Please leave as is.
} Steps, commands, or~configuration details from the accepted or highest-voted answer
    \item \textbf{\hl{Risks \& Limitations:} %MDPI: RESPONSE: Please leave as is.
} Cost issues, scaling problems, security vulnerabilities, deprecated features
    \item \textbf{\hl{Benefits \& Advantages:} %MDPI: RESPONSE: Please leave as is.
} Performance improvements, cost savings, reliability gains
    \item \textbf{\hl{Cloud Service(s) Involved:} %MDPI: RESPONSE: Please leave as is.
} Specific services (EC2, S3, IAM, AKS, GKE, Cloud Run,~Terraform)
    \item \textbf{\hl{Tools, Libraries, or~Frameworks:} %MDPI: RESPONSE: Please leave as is.
} Terraform, Ansible, Helm, Docker, CI/CD tools, SDK versions
    \item \textbf{\hl{Configuration Details:} %MDPI: RESPONSE: Please leave as is.
} YAML snippets, JSON configs, environment variables, CLI~commands
    \item \textbf{\hl{Performance Metrics or Benchmarks:} %MDPI: RESPONSE: Please leave as is.
} Scaling numbers, latency improvements, throughput, cost savings
    \item \textbf{\hl{Errors or Debugging Notes:} %MDPI: RESPONSE: Please leave as is.
} Error codes, stack traces, logs, common pitfalls
    \item \textbf{\hl{Security or Compliance Considerations:} %MDPI: RESPONSE: Please leave as is.
} IAM permissions, encryption, RBAC, firewall rules
    \item \textbf{\hl{Alternative Approaches:} %MDPI: RESPONSE: Please leave as is.
} Other solutions discussed, compared, or~rejected
    \item \textbf{\hl{Community Signals:} %MDPI: RESPONSE: Please leave as is.
} Acceptance status, upvote counts, author reputation, consensus~indicators
\end{enumerate}

This structured extraction ensures that technical details, code snippets, and~configuration parameters are preserved rather than summarized away, maintaining the actionability of Stack Overflow~solutions.

\textbf{\hl{5. Response Synthesis and Citation:} %MDPI: RESPONSE: Please leave as is.
}

The \texttt{FINAL\_RESPONSE\_PROMPT} governs how CloudNerd assembles Stack Overflow evidence into comprehensive technical answers. Key configuration elements include:

\textit{\hl{Evidence Selection Criteria:} %MDPI: RESPONSE: Please leave as is.
} The prompt requires selecting 8--20 posts, leaning toward more rather than fewer to ensure comprehensive coverage. It prioritizes high-quality posts (accepted answers, highly-upvoted answers, answers with reproducible code/configurations, answers referencing official documentation) while excluding weak evidence (speculation, opinion-only answers, solutions without reproducible steps).

\textit{\hl{Security-First Synthesis:} %MDPI: RESPONSE: Please leave as is.
} For questions involving security risks, cost traps, or~harmful misconfigurations, the~prompt instructs the system to focus on deterrence and warnings rather than enabling dangerous practices. The~goal is for CloudNerd to emphasize security best practices and compliance~considerations.

\textit{\hl{Multi-Provider Coverage:} %MDPI: RESPONSE: Please leave as is.
} \textls[-15]{The prompt encourages including solutions across different cloud providers (AWS, Azure, GCP) and frameworks (Kubernetes, Terraform, Docker) when relevant, providing users with comprehensive options rather than single-vendor~solutions.}

\textit{\hl{Accessibility Requirement:} %MDPI: RESPONSE: Please leave as is.
} Technical concepts must be explained in ways that junior engineers can understand, balancing technical precision with pedagogical~clarity.

Example output structure from the prompt:

\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
User Query: What is the best way to secure Kubernetes cluster access?

AI Answer:
Securing Kubernetes cluster access requires combining identity 
management, role-based access control (RBAC), and~network-layer 
protections, based on multiple solutions provided in the~evidence.

Access Control & Identity Management
Several posts emphasize enabling RBAC and assigning least-privilege 
roles, ensuring no user has cluster-admin by default [1][2].
Using OpenID Connect (OIDC) with external identity providers 
(Google, Azure AD, Okta) is repeatedly recommended [3].

Network Policies & Encryption
Multiple answers recommend implementing Network Policies to restrict 
pod-to-pod communication [4][5]. Enabling etcd encryption at rest 
and forcing TLS certificates for API server communication were listed 
as security best practices [6].

Risks & Trade-offs
One post warns that misconfigured RBAC rules can accidentally expose 
sensitive workloads if applied too broadly [9].

References:
[1] Stack Overflow. "How to use Kubernetes RBAC..." 
    https://stackoverflow.com/q/xxxxxx
[2] Stack Overflow. "Best practices for RBAC..." 
    https://stackoverflow.com/q/yyyyyy
...
\end{lstlisting}
\vspace{6pt}

Appendix~\ref{app:cloudnerd_final_response} contains the prompt~text.

\subsubsection{Environment Configuration}

The Environment Configuration Tab (see Appendix~\ref{app:web_interface_environment}) was used to set up external service integrations:

\textbf{\hl{LLM Provider Selection:} %MDPI: RESPONSE: Please leave as is.
} The system was configured to use \textbf{\hl{OpenAI} %MDPI: RESPONSE: Please leave as is.
} as the primary LLM provider by setting \texttt{LLM="OpenAI"} in \texttt{variables.env}. This selection determines which API client is initialized for all prompt~processing.

\textbf{\hl{API Keys Configuration:} %MDPI: RESPONSE: Please leave as is.
}
\begin{itemize}
    \item \texttt{OPENAI\_API\_KEY}: Required for GPT-4 API access. This key enables all five prompt categories (validation, query generation, relevance classification, article processing, response synthesis) to function.
    \item \texttt{STACK\_API\_KEY}: Optional but recommended for production use. Without~this key, the~Stack Exchange API limits requests to 300 per day; with the key, the~limit increases to 10,000 requests per day. For~a system processing 50 questions daily with 12 query variations each, this higher limit is essential for stable operation.
\end{itemize}

\subsubsection{User Interface Search Strategy Toggles}

The User Flow configuration of \NerdEngine exposes multiple retrieval options through toggleable search strategies (see Appendix~\ref{app:web_interface_user_flow}). The~settings chosen for CloudNerd~are:
\begin{itemize}
    \item \textbf{\hl{Upload Articles:} %MDPI: RESPONSE: Please leave as is.
} \texttt{visible: false}---PDF uploads disabled, as~Stack Overflow provides sufficient technical coverage for this domain.
    \item \textbf{\hl{Insert ID:} %MDPI: RESPONSE: Please leave as is.
} \texttt{visible: false}---Identifier-based search disabled, as~it is not applicable to cloud troubleshooting.
    \item \textls[-15]{\textbf{\hl{Search Stack Exchange:} %MDPI: RESPONSE: Please leave as is.
} \texttt{visible: true, defaultChecked: true}---Primary retrieval mechanism, enabled by default and labeled \hl{\emph{``Search using Stack Exchange Articles''}.} %MDPI: RESPONSE: Please leave as is.
}
\end{itemize}

Additional interface features were enabled to support transparency and~usability:
\begin{itemize}
    \item \textbf{\hl{Reference Section:} %MDPI: RESPONSE: Please leave as is.
} \texttt{visible: true}---Displays Stack Overflow citations for source verification.
    \item \textbf{\hl{Chat History:} %MDPI: RESPONSE: Please leave as is.
} \texttt{visible: true}---Preserves conversational context across troubleshooting sessions.
    \item \textbf{\hl{Query Cleaning:} %MDPI: RESPONSE: Please leave as is.
} \texttt{visible: true}---Activates JSON preprocessing for expanded query lists via \texttt{clean\_query.py}.
    \item \textbf{\hl{Deep Search:} %MDPI: RESPONSE: Please leave as is.
} \texttt{cascade\_retrieval.visible: true}---Enables \texttt{tiered\_collect\_ articles()} (planes P1--P5; Stack Overflow benchmark: P1 strict, P2 title-only, P4 extended-keyword). Configuration tab label \hl{\emph{Deep Search}.} %MDPI: RESPONSE: Please leave as is.

\end{itemize}

\subsubsection{Saved State File Listing}

After configuring all components through the web interface, the~CloudNerd application designer saved the setup as a named state using the Save State interface (see Appendix~\ref{app:web_interface_save_state}). The~repository includes \texttt{saved\_states/CloudNerd/} (compact Stack Overflow--only retrieval) and \texttt{saved\_states/CloudNerd Advanced/} (multi-source retrieval as in subsection~\ref{app:stackoverflow_retrieval_code}). A~typical bundle contains eight files; line counts and byte sizes vary slightly by export:

\begin{enumerate}
    \item \texttt{user\_env.js}: Frontend configuration including site name, icon, tagline, colors, disclaimer text, and~all UI toggles
    \item \texttt{openai\_prompts.py}: All five customized prompts with cloud-specific examples and validation rules
    \item \texttt{user\_search\_apis.py}: Stack Exchange integration with HTML cleaning; the Advanced variant adds RSS (\texttt{feedparser}), Azure/AWS/CloudFest discovery, parallel page fetch, and~resilient Stack API helpers (on the order of hundreds of lines)
    \item \texttt{clean\_query.py}: Single-function query parser (\texttt{clean\_query(query\_list, max \_queries=None)})
    \item \texttt{user\_list\_search.py}: Minimal PMID lookup (retained for framework compatibility)
    \item \texttt{variables.env}: API keys and LLM provider selection (optional \texttt{RATE\_LIMIT\_CURL \_RESILIENCE} for global HTTP resilience)
    \item \texttt{customnerd\_logo.png}: Cloud-themed logo image
    \item \texttt{historical\_answer.json}: Conversation history cache (if enabled)
\end{enumerate}

% -----------------------------------------------------------------
\subsection{CloudNerd retrieval: Stack Overflow APIs and cloud~RSS}
\label{app:stackoverflow_retrieval_code}

The retrieval component implements a \textbf{\hl{Stack Exchange} %MDPI: RESPONSE: Please leave as is.
} stage aligned with Stack Overflow’s data model, plus a \textbf{\hl{cloud RSS and page-fetch} %MDPI: RESPONSE: Please leave as is.
} stage. In~the first stage, the~Stack Exchange search API retrieves candidate questions for each expanded query. In~the second stage, the~highest-ranked answers for each unique question are fetched with the \texttt{withbody} filter, normalized, and~prepared for downstream relevance classification and synthesis. A~further stage gathers URLs from (i)~%MDPI: 1. We put these web in url tags, please chekck and confirm. 2. Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: confirmed the url tags; the vendor RSS/blog feeds (AWS, Azure, CloudFest) are software endpoints consumed by the retrieval code and were accessed on 25 June 2026 --- per-URL access dates can be added if MDPI requires.
% Author response: (1) Confirmed: \url{} tagging on the AWS, Azure, and CloudFest feed URLs is correct. (2) Access dates added to all three feed URLs in the standard MDPI format (accessed on 25 June 2026). These are publicly indexed RSS feeds maintained by the respective cloud providers.
 \url{https://aws.amazon.com/blogs/aws/feed/} (accessed on 25 June 2026), (ii)~\url{https://azure.microsoft.com/en-us/blog/feed/} (accessed on 25 June 2026) and Azure blog HTML search, (iii)~\url{https://www.cloudfest.com/feed/} (accessed on 25 June 2026), plus optional AWS site search and AWS resource-hub pages for selected tokens (e.g., Lambda, ECS, Kubernetes-related mappings). Entries are filtered by query-relevant token expansion; if nothing matches, recent feed items may be used. URLs are deduplicated, search-hub listing pages are skipped for body fetch, and~article HTML is retrieved in parallel; visible text is stored in \texttt{answer\_body} so records match the Stack Overflow shape (\texttt{question\_title}, URLs, optional \texttt{cloud\_news\_source}).

The number of Stack Overflow answers retained is controlled by \texttt{answer\_counter}; cloud breadth per provider is controlled by \texttt{cloud\_links\_per\_source}, with~a cap on total unique cloud URLs merged after all queries (e.g., $\min(30,\max(6,\lfloor\texttt{answer\_counter}/2\rfloor))$ in the reference implementation).

\paragraph{Central HTTP resilience.}
Shared logic in \texttt{helper\_functions.py} (activated by default at import, overridable via \texttt{RATE\_LIMIT\_CURL\_RESILIENCE} in \texttt{variables.env}) patches \texttt{requests.get} and \texttt{urllib.request.urlopen}. On~HTTP {429, 409, 503} or Stack Exchange JSON throttle indicators, the~backend may re-fetch the \emph{\hl{same} %MDPI: RESPONSE: Please leave as is.
} URL using \texttt{curl}. The~FastAPI pipeline wraps normal search with \texttt{collect\_articles\_with\_curl\_fallback}. \texttt{user\_search\_apis.py} adds in-module retries on 429/5xx, defensive JSON parsing (so empty or non-JSON bodies after throttling do not raise decode errors), honoring of API \texttt{backoff} fields, skipping of HTTP 400/404 answer fetches for invalid or removed threads, and~pacing between queries and between~questions.

\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
# Excerpt: resilient Stack Exchange helpers and collect_articles outline.
# The full module also defines search_all_rss, feedparser usage,
# parallel page fetch, and~HTML text extraction for cloud~URLs.

STACK_API_TIMEOUT = 35
STACK_INTER_QUERY_SLEEP = 2.0
STACK_BETWEEN_ANSWER_SLEEP = 0.25

def _stack_exchange_get(url, params, *, retries=5):
    delay = 2.0
    last = None
    for _ in range(retries):
        last = requests.get(
            url, params=params,
            headers={**HEADERS, "Accept": "application/json"},
            timeout=STACK_API_TIMEOUT,
        )
        if last.status_code == 429:
            wait = int(last.headers.get("Retry-After", delay))
            time.sleep(max(wait, delay))
            delay = min(delay * 1.5, 60)
            continue
        if last.status_code in (502, 503):
            time.sleep(delay)
            delay = min(delay * 1.5, 60)
            continue
        return last
    return~last

def _stack_exchange_payload(response):
    if response is None or not (response.text or "").strip():
        return None
    try:
        data = response.json()
    except ValueError:
        return None
    return data if isinstance(data, dict) else~None

def _stack_apply_backoff(data):
    if data and "backoff" in data:
        time.sleep(float(data["backoff"]))

def collect_articles(query_list, answer_counter=50, cloud_links_per_source=10):
    """Stack Overflow + AWS/Azure/CloudFest RSS and page text."""
    collected = []
    seen_questions = set()
    search_url = "https://api.stackexchange.com/2.3/search/advanced"
    answers_url = "https://api.stackexchange.com/2.3/questions/{ids}/ answers"
    # ... per-query search; parse with _stack_exchange_payload;
    #     skip on error_id or empty JSON; _stack_apply_backoff(data)
    # ... per-question answers; skip 400/404; append clean_html bodies
    # ... time.sleep(STACK_BETWEEN_ANSWER_SLEEP); STACK_INTER_QUERY_SLEEP
    stack_slice = collected[:answer_counter]
    cloud = []  # from search_all_rss + parallel GET + BeautifulSoup text
    return stack_slice + cloud
\end{lstlisting}

\paragraph{Design Rationale.}
\begin{itemize}
    \item \textbf{\hl{Two-stage Stack retrieval:} %MDPI: RESPONSE: Please leave as is.
} Separates question discovery from answer ranking, prioritizing community-validated solutions.
    \item \textbf{\hl{Deduplication:} %MDPI: RESPONSE: Please leave as is.
} The \texttt{seen\_questions} set prevents redundant processing when multiple query variants match the same question; cloud URLs are deduplicated across queries.
    \item \textbf{\hl{Metadata preservation:} %MDPI: RESPONSE: Please leave as is.
} Rich metadata enables citation generation, quality scoring, and~explainable synthesis.
    \item \textbf{\hl{Resilience and pacing:} %MDPI: RESPONSE: Please leave as is.
} In-module retries, safe JSON parsing, API \texttt{backoff} compliance, and~inter-request sleeps reduce throttling failures; global \texttt{curl} fallback still applies to patched \texttt{requests.get}.
    \item \textbf{\hl{Cloud supplement:} %MDPI: RESPONSE: Please leave as is.
} RSS and page fetch broaden evidence beyond Q\&A while reusing the same downstream classification and extraction prompts.
    \item \textbf{\hl{Configurable scope:} %MDPI: RESPONSE: Please leave as is.
} \texttt{answer\_counter} and \texttt{cloud\_links\_per\_source} trade coverage against latency and tokens.
\end{itemize}

% -----------------------------------------------------------------
\subsection{Query Refinement and JSON~Sanitization}
\label{app:query_refinement_code}

Query expansion prompts produce structured JSON output containing multiple rewritten search queries. This preprocessing stage extracts the expanded query list and converts it into a plain-text format suitable for execution against the Stack Exchange API. Defensive parsing ensures graceful degradation in the presence of malformed LLM~output.

\vspace{12pt}

\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
def clean_query(query_list, max_queries=None):
    """
    Turn query_generation output into a flat list of search strings.
    Parses JSON expanded_queries or nested plain-text lists.
    Optional max_queries caps how many strings are returned.
    """
    if not query_list:
        return []
    queries = _parse_expanded_queries(query_list)
    if not queries:
        queries = _flatten_query_list(query_list)
    if max_queries is not None and max_queries > 0:
        return queries[:max_queries]
    return queries
\end{lstlisting}

\paragraph{Design Rationale.}
\begin{itemize}
    \item Enables programmatic extraction of LLM-generated query expansions.
    \item Prevents pipeline failures due to malformed or partially structured JSON.
    \item Ensures downstream retrieval operates on clean, normalized query strings.
\end{itemize}

\section{CloudNerd Evaluation~Pipeline}
\label{app:cloudnerd_eval_pipeline}

This section documents the CloudNerd evaluation configuration deployed for benchmark runs. It replaces the \texttt{CloudNerd Advanced} multi-source RSS bundle while preserving the same downstream relevance classification, extraction, and~synthesis contract. Repository filenames retain internal version suffixes for reproducibility; main-text nomenclature follows Section~\ref{sec:eval_setup}.

\subsection{Retrieval and synthesis flags (120-question benchmark)}

Multi-plane tiered Stack Overflow retrieval is controlled by two~layers:
\begin{enumerate}
  \item \textbf{\hl{Code profile:} %MDPI: RESPONSE: Please leave as is.
} \texttt{set\_active\_nerd\_profile(state\_name)} on \texttt{load\_state} loads \texttt{RETRIEVAL\_NERD\_PROFILES["CloudNerd"]} from \texttt{helper\_functions.py} (\texttt{mode: "cascade"}, domain \texttt{lexicon}, plane budgets, default planes P1--P5). Unregistered saved states fall back to \texttt{RETRIEVAL\_DEFAULTS} (\texttt{mode: "legacy"}).
  \item \textbf{\hl{User Flow toggle:} %MDPI: RESPONSE: Please leave as is.
} \texttt{cascade\_retrieval.visible} in \texttt{user\_env.js} (Configuration tab: \emph{Deep Search}) must be \texttt{true}; otherwise \texttt{is\_cascade\_mode()} returns false and the pipeline uses legacy single-pass \texttt{collect\_articles()} regardless of profile.
\end{enumerate}

Legacy \texttt{RETRIEVAL\_*} environment variables remain supported when no registered Nerd profile matches the loaded state name; they are no longer required for CloudNerd or DietNerd~deployments.

\begin{itemize}
  \item Multi-plane tiered retrieval enabled (\texttt{tiered\_collect\_articles}, profile + Deep Search toggle)
  \item \texttt{CITATION\_RESCUE\_ENABLED=true}---TF-IDF rescue for posts dropped by the relevance~classifier
  \item \texttt{RETRIEVAL\_GAP\_FILL\_ENABLED=true}---supplemental Stack Overflow search when coverage is thin (also plane~P5 inside tiered retrieval)
  \item \texttt{ENABLE\_QUERY\_FALLBACK=false}; \texttt{FAITHFULNESS\_TWO\_PASS\_ENABLED=false}
  \item \texttt{FINAL\_RESPONSE\_TEMPERATURE=0.2}; evaluated synthesis prompts (Appendix~\ref{app:cloudnerd_eval_prompts})
\end{itemize}

\paragraph{API keys template (\texttt{variables.env}).}

\vspace{6pt}
\begin{lstlisting}[breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
OPENAI_API_KEY="your-openai-key-here"
STACK_API_KEY="your-stack-exchange-key-here"
LLM="OpenAI"
\end{lstlisting}

\subsection{Multi-Plane Tiered Retrieval (\texttt{tiered\_collect\_articles})}
\label{app:tiered_retrieval}

\textls[-35]{When Deep Search is enabled, \texttt{main.py} invokes \texttt{tiered\_collect\_articles(collect\_fn, input\_text, p1\_query\_list)}} in \texttt{helper\_functions.py}. The~function merges results across enabled planes (default P1--P5), tags each article with its \texttt{retrieval\_plane}, applies quality gates between planes, optionally short-circuits when coverage is sufficient, and~returns \texttt{(articles, metadata)} with \texttt{planes\_run}, \texttt{queries\_per\_plane}, \texttt{retrieval\_confidence}, and~per-plane quality-gate~diagnostics.

\paragraph{Retrieval planes.}
\begin{enumerate}
  \item \textbf{\hl{P1 (precision):} %MDPI: RESPONSE: Please leave as is.
} Queries from \texttt{clean\_query()} over LLM-expanded strings. On~Stack Overflow this is the \emph{\hl{strict} %MDPI: RESPONSE: Please leave as is.
} tier (full body/tag match).
  \item \textls[-25]{\textbf{\hl{P2 (relaxed):} %MDPI: RESPONSE: Please leave as is.
} Title- and keyword-oriented variants from \texttt{\_cascade\_generate\_queries()}} on the resolved question title. Maps to the \emph{\hl{title-only} %MDPI: RESPONSE: Please leave as is.
} tier on Stack Overflow.
  \item \textbf{\hl{P3 (facet):} %MDPI: RESPONSE: Please leave as is.
} Re-runs P1/P2 seeds with a domain facet---CloudNerd lexicon tokens or DietNerd \texttt{[Humans]} PubMed suffix. Contributes domain-scoped recall before broad expansion.
  \item \textbf{\hl{P4 (broad):} %MDPI: RESPONSE: Please leave as is.
} Additional batches from \texttt{GENERAL\_QUERY\_PROMPT}, a~cascade broad-query prompt, and~deterministic backup strings. Maps to \emph{\hl{extended-keyword} %MDPI: RESPONSE: Please leave as is.
} expansion on Stack Overflow.
  \item \textbf{\hl{P5 (gap-fill):} %MDPI: RESPONSE: Please leave as is.
} Queries built from key terms absent in the merged corpus (\texttt{build\_gap\_ fill\_queries}), overlapping with supplemental \texttt{retrieval\_gap\_fill.py} logic used in benchmark~runs.
\end{enumerate}

On the 120-question CloudNerd benchmark, the~best plane before stopping was P1 (strict) on 84 questions, P2 (title-only) on 33, and~P4-class extended-keyword on 3; plane~P5 and post-cascade gap-fill activated on additional questions when coverage remained~thin.

\paragraph{Entry point (excerpt).}

\vspace{6pt}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
def tiered_collect_articles(collect_fn, input_text, p1_query_list, *, title_text=None):
    """
    Run planes P1->P5. Returns (articles, metadata).

    metadata keys: retrieval_mode, planes_run, planes_log,
    queries_per_plane, retrieval_confidence, quality_gate
    """
    # P1 precision -> P2 relaxed -> P3 facet -> P4 broad -> P5 gap-fill
    # Quality gates may short-circuit when passes_retrieval_quality_gate() succeeds
\end{lstlisting}

\paragraph{Profile registry (excerpt).}
\vspace{6pt}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
RETRIEVAL_NERD_PROFILES = {
    "DietNerd": {"mode": "cascade", "facet_suffix": "[Humans]"},
    "CloudNerd": {"mode": "cascade", "lexicon": "aws,amazon,azure,..."},
    "NewsNerd": {"mode": "legacy"},
}
\end{lstlisting}

\subsection{Supplemental Retrieval (\texttt{retrieval\_gap\_fill.py})}

After tiered retrieval (planes P1--P5) completes, supplemental retrieval triggers one additional Stack Overflow search when (i) strict-tier (P1) results contain $\leq 2$ articles, or~(ii) key terms from the user question are absent from retrieved post text. Supplemental retrieval activated on 36 of 120 test questions. An~expanded supplemental-retrieval criterion (paired with an alternative prompt variant) failed the development-set quality gate and was not~deployed.

\paragraph{Trigger logic and retrieval (\texttt{should\_gap\_fill}, \texttt{gap\_fill\_retrieval}).}
\vspace{6pt}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
def should_gap_fill(articles, query, title, cascade_meta):
    meta = cascade_meta or {}
    terms = extract_key_terms(query, title)
    miss = missing_terms(terms, articles)

    thin_strict = (
        meta.get("retrieval_mode") == "strict" and len(articles) <= 2
    )
    low_coverage = bool(terms) and len(miss) / len(terms) >= 0.5

    if thin_strict or low_coverage:
        return True, miss
    return False, miss


def gap_fill_retrieval(articles, query, title, max_date, collect_fn, cascade_meta=None):
    trigger, miss = should_gap_fill(articles, query, title, cascade_meta)
    if not trigger:
        return articles, {"gap_fill_used": False, "missing_terms": miss}

    queries = build_gap_fill_queries(query, title, miss)
    extra = collect_fn(queries, max_date=max_date) or []
    merged = merge_articles(articles, extra)
    return merged, {
        "gap_fill_used": True,
        "gap_fill_queries": queries,
        "gap_fill_added": max(0, len(merged) - len(articles)),
    }
\end{lstlisting}

\subsection{Citation Pipeline Rescue (\texttt{citation\_rescue.py})}

The relevance classifier can drop posts that still contain usable technical content (e.g., Docker, Kubernetes, Terraform, LibreOffice deployment threads). When citation recovery is enabled, TF-IDF overlap between the user question and dropped posts re-inserts the best-matching articles before synthesis. Combined with a softer \texttt{RELEVANCE\_CLASSIFIER\_PROMPT} (Appendix~\ref{app:cloudnerd_eval_prompts}), this raised the development-set scorable rate from 6/20 to 20/20 (Table~\ref{tab:devset-pipeline-evolution}).

\paragraph{TF-IDF rescue and citation backfill (\texttt{rescue\_relevant\_articles}, \texttt{ensure\_citations}).}
\vspace{6pt}
\begin{lstlisting}[language=python,breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
def rescue_relevant_articles(articles, user_query, *, top_k=8):
    """Keep top-k organized articles by TF-IDF similarity when relevance filter returns none."""
    vectorizer = TfidfVectorizer()
    matrix = vectorizer.fit_transform(texts + [user_query])
    query_vec = matrix[-1]
    scores = cosine_similarity(matrix[:-1], query_vec).flatten()
    ranked = sorted(zip(scores, articles), key=lambda x: x[0], reverse=True)
    return [article for score, article in ranked[:top_k] if score > 0]


def ensure_citations(final_output, articles, citations):
    """Fill citations when reference parsing failed but organized articles exist."""
    if citations:
        return citations
    if not articles:
        return []
    print("[CITATION_RESCUE] Forcing citations from organized articles ...")
    return build_citations_from_articles(articles)
\end{lstlisting}

\subsection{Evidence-Constrained Synthesis~Prompt}

The \texttt{FINAL\_RESPONSE\_PROMPT} enforces direct-answer-first structure, never-refuse behavior when evidence is non-empty, and~four grounding rules (no invented config keys; reuse question terms in sentence 1; no cross-snippet synthesis unless one post states the conclusion; gap statements last). The~complete prompt text appears in Appendix~\ref{app:cloudnerd_eval_prompts}.

\section{Questionnaire-Based~Personalization}\label{personalization}

%\textbf{\textcolor{red}{VERIFY --- prompts in this section should use special font (lstlisting/texttt) consistently.}{\textcolor{blue}{ --- Done}}}

This appendix shows the prompts used to fill in a questionnaire given that a query has mentioned a disease. The~user will then be queried about those risk~factors.
\subsection{Large Language Model~Prompts}\label{personalization_prompts}
\vspace{6pt}

\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
You are given a text describing disease risk factors. Your task is to extract only the most common and high-risk factors, excluding redundant or weakly associated~ones.

Format your output strictly as concise questionnaire items:
[risk factor]: [short categorical options]

Rules:
1. No personal pronouns or directional words (e.g., "you", "your", "patient").
2. Each option must be short - ideally one or two words (e.g., "yes / no", "25-35").
3. Focus on strong, well-established risk factors only.
4. Use clear, medically neutral language.
5. Output only the final questionnaire lines. No~explanation.

Example Output:
- Family history of AATD: yes, no
- Family history of COPD or emphysema: yes, no
- Family history of liver disease: yes, no
- Cigarette smoking duration (years): <5, 5-10, 10-20, >20
- Cigarette smoking intensity (packs/day): <1, 1-2, >2

Now, given the following text, output the~questionnaire

\end{lstlisting}

\subsection{ Example Outputs Presented to Users Based on Different Answers to Different~Questionnaires}\label{personalization_outcomes}
\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]


Question 1: Risk factors for miscarriage
Questionnaire 1: 

Age: 40-45
Past Miscarriages: no
Long-Term Conditions: yes
Uterine Or Cervical Problems: no
Use Of Tobacco: yes


Output~1

Analysis and Recommendations: Based on the available research, several risk factors are associated with an increased likelihood of miscarriage, which are particularly relevant given your age group (40-45 years old) and tobacco use. Here's a detailed look at these factors:

    Maternal Age: Research consistently shows that maternal age is a significant risk factor for miscarriage. The~risk of miscarriage increases with age, particularly after age 35, and~even more so after age 40. One study found that the risk of fetal loss was significantly high in women aged 30 years and above, with~a notable increase after age 40 [4]. Another study highlighted that miscarriage risk reaches up to 53% at age 45 and over.
    Tobacco Use: Smoking is widely recognized as a risk factor for miscarriage. The~chemicals in tobacco can affect the cervix and the health of your eggs, increasing the risk of miscarriage. Although~the specific articles reviewed do not focus on tobacco use, the~general medical consensus supports this risk factor.
    Long-term Health Conditions: Depending on the nature of your long-term conditions, these could potentially influence miscarriage risk. Conditions such as diabetes, thyroid disorders, and~hypertension are known to increase miscarriage risks if not well-managed during pregnancy.
    Previous Pregnancy Outcomes: While you mentioned no history of past miscarriages, other adverse outcomes in previous pregnancies (if any), such as stillbirth or preterm delivery, can also increase the risk of miscarriage in subsequent~pregnancies.


Given these factors, it's crucial to discuss your specific health profile and any long-term conditions with a healthcare provider who can offer personalized advice and management strategies. They might recommend lifestyle adjustments, closer monitoring during pregnancy, or~specific medical interventions to mitigate these~risks.

In summary, advancing maternal age and tobacco use are significant risk factors for miscarriage in your case. Managing long-term health conditions effectively and seeking regular prenatal care are essential steps in reducing the risk of miscarriage. Always consult with a healthcare professional for guidance tailored to your specific health~needs.

Questionnaire 2:

    Age: under 35
    Past Miscarriages: yes
    Long-Term Conditions: no
    Uterine Or Cervical Problems: yes
    Use Of Tobacco: no


Output~2

Analysis and Recommendations: Based on the analysis of several studies, various risk factors have been identified that contribute to the likelihood of experiencing a miscarriage. Given your specific condition, here are the relevant factors:


    Maternal Age: Although you mentioned being under 35, it's important to recognize that increased maternal age is generally associated with higher miscarriage rates. However, women under 35 generally have lower risk compared to older age groups.
    History of Past Miscarriages: Having had previous miscarriages is a significant risk factor. Studies show a clear pattern where the risk of miscarriage increases with the number of prior miscarriages.
    Uterine or Cervical Problems: Structural problems with the uterus or cervix can increase the risk of miscarriage. While specific details about these conditions weren't covered in the evaluated studies, medical literature widely acknowledges this as a risk factor.
    Paternal Age: Advanced paternal age (over 40 years) is also linked to increased risks of miscarriage. This might be relevant depending on the age of your partner.
    Other Identified Factors: Additional factors identified include obesity, high parity (having had many pregnancies), and~previous cesarean deliveries. While these may not apply directly to you since you haven't mentioned them, they are important for a comprehensive understanding of the~risks.


Considering these factors, here are some suggestions for managing and potentially mitigating the risk of miscarriage:


    Regular Medical Follow-up: Given your history of miscarriages and uterine or cervical issues, regular check-ups with a healthcare provider specializing in maternal-fetal medicine can help manage your risk.
    Healthy Lifestyle: Maintaining a healthy weight, managing stress, and~ensuring adequate nutrition can support pregnancy health.
    Preconception Counseling: Discussing your full medical history with a healthcare provider can provide tailored advice and potential interventions to reduce~risk.


In conclusion, while certain factors like past miscarriages and uterine or cervical problems increase your risk, proactive medical care and lifestyle management can help mitigate some of these risks. It is essential to work closely with your healthcare provider to monitor and manage your pregnancy based on your specific health~profile.

Question 2: Suggestions for heart disease
Questionnaire 1: 

    Age: <55
    Gender: female
    Family history of heart disease: no
    Smoke: yes
    Diet With High Sugar Or Fat: yes


Output~1

Analysis and Recommendations: Given your profile as a female under 55, who smokes and has a diet high in sugar or fat, it is crucial to address these risk factors to effectively manage and prevent heart disease. Here are some personalized suggestions based on robust research findings:


    Adopt a Mediterranean Diet: Research shows that the Mediterranean diet, which is rich in fruits, vegetables, whole grains, and~healthy fats (mainly from olive oil), can significantly reduce the risk of cardiovascular diseases (CVDs), including heart attacks and strokes. This diet also supports longevity and reduces the overall mortality from heart disease.
    Incorporate Plant-Based Foods: Studies indicate that plant-based diets can lower the risk of coronary heart disease and reduce factors such as body mass index (BMI), cholesterol, and~markers of inflammation. However, it's important to note that while plant-based diets are associated with lower cardiovascular mortality, they may carry a higher risk of certain types of strokes. It would be beneficial to balance your diet with a variety of foods to mitigate this risk.
    Reduce Smoking: Smoking is a significant risk factor for CVD. Quitting smoking can drastically reduce your risk of heart disease and improve overall cardiovascular health. Consider seeking support through smoking cessation programs which can offer guidance and increase your chances of success.
    Manage Dietary Fats and Sugars: High intake of fats and sugars, especially trans fats and added sugars, can contribute to heart disease. Focus on reducing these in your diet and replace them with healthier alternatives like whole grains and healthy fats (e.g., fats from fish and olive oil).
    Regular Physical Activity: Engaging in regular physical activity, such as brisk walking, cycling, or~swimming, can help manage weight, reduce hypertension, and~strengthen your cardiovascular system. Aim for at least 150 min of moderate-intensity aerobic physical activity or 75 min of vigorous-intensity activity per week, as~recommended by health guidelines.
    Monitor Cardiovascular Health Regularly: Regular check-ups with your healthcare provider for blood pressure, cholesterol levels, and~other relevant screenings are crucial. This is especially important given your smoking habit and dietary preferences, which can elevate risk factors for heart disease.
    Seek Professional Dietary Advice: Consulting with a registered dietitian or nutritionist can provide you with personalized dietary advice that considers your specific health needs, lifestyle, and~dietary preferences. This professional guidance can help you make effective and sustainable dietary~changes.


By addressing these key areas, you can significantly reduce your risk of heart disease and improve your overall~health.

Questionnaire~2

    Age: <55
    Gender: female
    Family history of heart disease: yes
    Smoke: no
    Diet With High Sugar Or Fat: no


Output~2

Analysis and Recommendations: Given your specific conditions as a female under 55 with a family history of heart disease, and~the fact that you smoke but maintain a diet not high in sugar or fat, here are some tailored suggestions for heart disease prevention and management:

    Adopt a Heart-Healthy Diet: Mediterranean Diet: Studies have shown that a Mediterranean diet, rich in fruits, vegetables, whole grains, and~healthy fats like olive oil, can significantly reduce the risk of major cardiovascular events.
    Plant-Based Diet: Incorporating more plant-based foods such as legumes, nuts, and~seeds has been associated with lower rates of cardiovascular mortality. However, be cautious as some studies have noted an increased risk of stroke among vegetarians.
    Regular Physical Activity: Engage in regular physical activity, which has been shown to improve heart health and can help mitigate the risk factors associated with familial heart disease. Aim for at least 1150 min of moderate-intensity aerobic exercise per week.
    Monitor Cardiovascular Health Regularly: Regular check-ups can help monitor heart health, especially important for someone with a family history of heart disease. This includes checking cholesterol levels, blood pressure, and~other relevant cardiovascular risk factors.
    Address Other Risk Factors: While your diet is not high in sugar or fat, ensuring a balanced intake of nutrients and maintaining a healthy weight are important. Avoid trans fats and reduce sodium intake to further decrease heart disease risk.
    Psychological and Emotional Well-being:Stress management and maintaining a positive mental health state play roles in cardiovascular health. Consider activities like yoga, meditation, or~other stress-relieving practices.
    Consider Nutritional Counseling: Consulting with a registered dietitian or nutritionist can provide personalized dietary advice, ensuring that your dietary choices align with your heart health goals and nutritional~needs.


By focusing on these areas, you can effectively manage and potentially reduce your risk of developing heart disease, considering your specific risk factors and~lifestyle.



\end{lstlisting}

\section{DietNerd Large Language Model~Prompts}\label{DietNerdLLM}
\unskip
\subsection{\texttt{generate\_final\_response\_v2}}\label{generate_response}

\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
You are an expert in evaluating research articles and summarizing findings based on the strength of evidence. Your task is to review the Abstract and Summary sections of the provided articles and use only this information to answer the user's question. Always use every article provided to give a comprehensive and robust~answer.

The response should include sections relevant to the topic, and~information should be written in bullet point form with asterisks. Sections should be separated by~"----".

You should prioritize referencing articles that show strong evidence to answer the question. Strong evidence means the research is well-conducted, peer-reviewed, human-focused, widely accepted in the scientific community, utilizes large sample sizes, employs randomized controlled trials (RCTs) when applicable, and~includes meta-analyses or systematic reviews when available. Prioritize recent publications. Provide a direct, research-backed answer to the question and focus on identifying the pros and cons of the topic in question. The~answer should highlight when there are potential risks or dangers~present.

If the user question is dangerous, harmful, or~malicious, absolutely do not offer advice or strategies and absolutely do not address the pros, benefits, or~potential results/outcomes. You must only focus on deterring this behavior, addressing the risks, and~offering safe alternatives. The~answer should also try to include as many different demographics as possible. Absolutely no animal studies should be referenced or included in the final response. Mention dosage amounts when information is available. Medical terms and technical concepts must be explained to a layman audience. Be sure to emphasize that you should always go and see a registered dietitian or a registered dietitian~nutritionist.

The output must follow this format:

When you state a specific claim or piece of information derived from one or more of the provided articles, you must immediately follow it with an inline citation in Vancouver style, indicating the source(s) in brackets (e.g., "[1]", "[2, 5]", "[3-7]"). Ensure that each distinct piece of information is attributed to its correct source(s).

----

[1] <AMA_citation_1>
[2] <AMA_citation_2>
[3] <AMA_citation_3>
[4] <AMA_citation_4>
[5] <AMA_citation_5>
[6] <AMA_citation_6>
[7] <AMA_citation_7>
[8] <AMA_citation_8>
[9] <AMA_citation_9>
[10] <AMA_citation_10>

Here are some examples:

User: {example_1_question}
AI: {example_1_response}

User: {example_2_question}
AI: {example_2_response}
\end{lstlisting}

\subsection{\texttt{summarize\_output}}\label{summarize_output}


\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
You are an expert at summarizing research articles and maintaining proper citations. Your task is to take the user-provided text, which is an output from a previous LLM response, and~summarize~it.

Core Instructions:
- Remove Repetition: Identify and remove redundant phrases, sentences, or~paragraphs.
- Maintain All Original Information: Do not delete any unique pieces of information.
- Preserve Citations: This is the most important rule. If~you remove a piece of information that is cited, you must append its citation to the section where the same information has been kept.
- Format: The final output must follow the original format, including the sections separated by  ----- and the full citation list at the end.
- Inline Citations: Ensure every specific claim or piece of information is immediately followed by a correct inline citation in Vancouver style (e.g., ``[1]'', ``[2, 5]'').

Example Format to Follow:

A key piece of information derived from one or more of the provided articles must be immediately followed by an inline citation in brackets [1][2]. Ensure that each distinct piece of information is attributed to its correct source(s).

-----

[1] <AMA_citation_1>
[2] <AMA_citation_2>
[3] <AMA_citation_3>
...

Do not output the example citations; follow the structure but use the citations from the original text~provided.

\end{lstlisting}

\begin{table}[H]

%
\caption{Output comparison for the query ``What are the effects of the keto diet?''.}
\label{tab:keto-output-comparison}
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.2}
\begin{tabularx}{\textwidth}{>{\raggedright\arraybackslash}m{2.1cm}>{\raggedright\arraybackslash}X>{\raggedright\arraybackslash}X}
\hline
\textbf{Bullet Point Title} & \textbf{Pre Summarize\_Output} & \textbf{Post Summarize\_Output} \\
\hline
\textbf{\hl{Weight Loss} %MDPI: RESPONSE: Please leave as is.
} &
The ketogenic diet can lead to significant short-term weight \hl{loss} %MDPI: We have modified the text to include links. Please review and confirm. RESPONSE: confirmed.
 \cite{guu2020realm,izacard2021leveraging,nguyen2025marag}. A~meta-analysis found greater long-term weight loss with a VLCKD compared to a low-fat diet \cite{guu2020realm}. KD may suppress appetite via ketones and reduced intake of processed carbohydrates \cite{dietnerd2024}. It also improves waist circumference, HbA1c, triglycerides, and~HDL, especially in people with type 2 diabetes \cite{zhang2025osc}. Long-term weight-loss efficacy is less significant \cite{ji_hallucination_nlg_2023}. &
The KD induces significant short-term weight loss \cite{guu2020realm,izacard2021leveraging,nguyen2025marag,dietnerd2024,bioengineering12080895,zhang2025osc}, possibly due to appetite suppression and reduced processed carbohydrate intake \cite{dietnerd2024}. A~meta-analysis found greater long-term weight loss with VLCKD vs. low-fat diets \cite{guu2020realm}. It improves waist circumference, glycemic control, and~lipid profiles, especially in overweight individuals with type 2 diabetes \cite{zhang2025osc}. Long-term weight-loss effects remain modest \cite{ji_hallucination_nlg_2023}. \\
\hline
\textbf{\hl{Gut Microbiota} %MDPI: RESPONSE: Please leave as is.
} &
The ketogenic diet influences gut microbiota, with~studies showing both increases in beneficial bacteria and decreases in microbial richness and diversity \cite{chase2023langchain}. It affects metabolism, nutrition, and~immune and nervous system development \cite{nguyen_minp_sampling_2025}. Short-term effects may be beneficial, but~long-term dysbiosis risk remains uncertain \cite{nguyen_minp_sampling_2025}. &
The KD affects gut microbiota, influencing metabolism, nutrition, and~immune and nervous system development \cite{chase2023langchain,nguyen_minp_sampling_2025}. Short-term benefits are suggested, but~long-term consequences and potential dysbiosis remain unclear \cite{nguyen_minp_sampling_2025}. \\
\hline
\end{tabularx}

\end{table}

\begin{table}[H]\ContinuedFloat  

%
\caption{\emph{Cont.}}
\label{tab:keto-output-comparison}
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.2}
\begin{tabularx}{\textwidth}{>{\raggedright\arraybackslash}m{2.1cm}>{\raggedright\arraybackslash}X>{\raggedright\arraybackslash}X}
\hline
\textbf{Bullet Point Title} & \textbf{Pre Summarize\_Output} & \textbf{Post Summarize\_Output} \\
\textbf{\hl{Neurological Effects} %MDPI: RESPONSE: Please leave as is.
} &
The KD is effective for epilepsy treatment, particularly in children and adolescents, reducing seizure frequency and potentially improving cognition and behavior \cite{SignificantGravitas_AutoGPT_2023,Moura_CrewAI_2024}. Early research suggests possible benefits for Alzheimer’s disease, though~more evidence is needed \cite{langchain_odr_2025}. It also shows promise in treating drug-resistant epilepsy and neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and~multiple sclerosis, potentially via improved mitochondrial function and reduced oxidative stress \cite{gao2024rag,wiher_decoding_strategies_2022,wang_self_consistency_cot_2023}. &
The KD effectively treats drug-resistant epilepsy \cite{SignificantGravitas_AutoGPT_2023,Moura_CrewAI_2024,wang_self_consistency_cot_2023} and may improve cognition and behavior \cite{Moura_CrewAI_2024}. Evidence suggests potential benefits for Alzheimer’s disease but requires further research \cite{langchain_odr_2025,gao2024rag,wiher_decoding_strategies_2022}. It also shows promise for Parkinson’s and multiple sclerosis by improving mitochondrial function, reducing oxidative stress, and~modulating inflammation \cite{gao2024rag,wiher_decoding_strategies_2022}. \\
%\hline
%\textbf{Cardiovascular Effects} &
%The KD can improve triglycerides and HDL cholesterol in the short term \cite{dietnerd2024,hu2025agentic}, but~may increase LDL cholesterol, potentially raising long-term cardiovascular risks \cite{dietnerd2024,yang2025dsrag}. While it may improve blood lipid profiles and blood pressure \cite{ravfogel_conformal_nucleus_sampling_2023}, concerns exist regarding high saturated fat intake over time \cite{ji_hallucination_nlg_2023}. Ketone bodies may provide anti-inflammatory and cardioprotective effects \cite{ravfogel_conformal_nucleus_sampling_2023}. &
%The KD improves triglycerides and HDL in the short term \cite{dietnerd2024,hu2025agentic} but may raise LDL cholesterol and increase long-term cardiovascular risk \cite{dietnerd2024,yang2025dsrag}. It can improve lipid profiles and blood pressure \cite{ravfogel_conformal_nucleus_sampling_2023}, yet concerns persist about long-term saturated fat intake \cite{ji_hallucination_nlg_2023}. Ketone bodies may have cardioprotective benefits, but~KD does not meet all criteria for a healthy diet \cite{ji_hallucination_nlg_2023}. \\

%\hline
%\textbf{Weight Loss} &
%The ketogenic diet can lead to significant short-term weight \hl{loss} %MDPI: We have modified the text to include links. Please review and confirm.
% \cite{guu2020realm,izacard2021leveraging,nguyen2025marag}. A~meta-analysis found greater long-term weight loss with a VLCKD compared to a low-fat diet \cite{guu2020realm}. KD may suppress appetite via ketones and reduced intake of processed carbohydrates \cite{dietnerd2024}. It also improves waist circumference, HbA1c, triglycerides, and~HDL, especially in people with type 2 diabetes \cite{zhang2025osc}. Long-term weight-loss efficacy is less significant \cite{ji_hallucination_nlg_2023}. &
%The KD induces significant short-term weight loss \cite{guu2020realm,izacard2021leveraging,nguyen2025marag,dietnerd2024,bioengineering12080895,zhang2025osc}, possibly due to appetite suppression and reduced processed carbohydrate intake \cite{dietnerd2024}. A~meta-analysis found greater long-term weight loss with VLCKD vs. low-fat diets \cite{guu2020realm}. It improves waist circumference, glycemic control, and~lipid profiles, especially in overweight individuals with type 2 diabetes \cite{zhang2025osc}. Long-term weight-loss effects remain modest \cite{ji_hallucination_nlg_2023}. \\
%\hline
%\textbf{Gut Microbiota} &
%The ketogenic diet influences gut microbiota, with~studies showing both increases in beneficial bacteria and decreases in microbial richness and diversity \cite{chase2023langchain}. It affects metabolism, nutrition, and~immune and nervous system development \cite{nguyen_minp_sampling_2025}. Short-term effects may be beneficial, but~long-term dysbiosis risk remains uncertain \cite{nguyen_minp_sampling_2025}. &
%The KD affects gut microbiota, influencing metabolism, nutrition, and~immune and nervous system development \cite{chase2023langchain,nguyen_minp_sampling_2025}. Short-term benefits are suggested, but~long-term consequences and potential dysbiosis remain unclear \cite{nguyen_minp_sampling_2025}. \\
%\hline
%\textbf{Neurological Effects} &
%The KD is effective for epilepsy treatment, particularly in children and adolescents, reducing seizure frequency and potentially improving cognition and behavior \cite{SignificantGravitas_AutoGPT_2023,Moura_CrewAI_2024}. Early research suggests possible benefits for Alzheimer’s disease, though~more evidence is needed \cite{langchain_odr_2025}. It also shows promise in treating drug-resistant epilepsy and neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and~multiple sclerosis, potentially via improved mitochondrial function and reduced oxidative stress \cite{gao2024rag,wiher_decoding_strategies_2022,wang_self_consistency_cot_2023}. &
%The KD effectively treats drug-resistant epilepsy \cite{SignificantGravitas_AutoGPT_2023,Moura_CrewAI_2024,wang_self_consistency_cot_2023} and may improve cognition and behavior \cite{Moura_CrewAI_2024}. Evidence suggests potential benefits for Alzheimer’s disease but requires further research \cite{langchain_odr_2025,gao2024rag,wiher_decoding_strategies_2022}. It also shows promise for Parkinson’s and multiple sclerosis by improving mitochondrial function, reducing oxidative stress, and~modulating inflammation \cite{gao2024rag,wiher_decoding_strategies_2022}. \\
\hline
\textbf{\hl{Cardiovascular Effects} %MDPI: RESPONSE: Please leave as is.
} &
The KD can improve triglycerides and HDL cholesterol in the short term \cite{dietnerd2024,hu2025agentic}, but~may increase LDL cholesterol, potentially raising long-term cardiovascular risks \cite{dietnerd2024,yang2025dsrag}. While it may improve blood lipid profiles and blood pressure \cite{ravfogel_conformal_nucleus_sampling_2023}, concerns exist regarding high saturated fat intake over time \cite{ji_hallucination_nlg_2023}. Ketone bodies may provide anti-inflammatory and cardioprotective effects \cite{ravfogel_conformal_nucleus_sampling_2023}. &
The KD improves triglycerides and HDL in the short term \cite{dietnerd2024,hu2025agentic} but may raise LDL cholesterol and increase long-term cardiovascular risk \cite{dietnerd2024,yang2025dsrag}. It can improve lipid profiles and blood pressure \cite{ravfogel_conformal_nucleus_sampling_2023}, yet concerns persist about long-term saturated fat intake \cite{ji_hallucination_nlg_2023}. Ketone bodies may have cardioprotective benefits, but~KD does not meet all criteria for a healthy diet \cite{ji_hallucination_nlg_2023}. \\
\hline
\end{tabularx}

\end{table}


\section{Modifications of Open Deep Research for Stack Overflow~Experiment} \label{OpenDeepResearchmods}



%The most significant modification involved replacing 
Open Deep Research's web search capabilities is based on \texttt{get\_all\_tools()} function in utils.py conditionally loaded search tools based on the configured SearchAPI enum (TAVILY, PUBMED, OPENAI, ANTHROPIC). We introduced a new \texttt{SearchAPI\.JSONL} option and implemented the corresponding \texttt{jsonl\_search} tool.

\subsection{\highlighting{JSONL Search Implementation} %MDPI: We made it heading 2, please check all. RESPONSE: confirmed.
}

\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
async def jsonl_search(
    queries: List[str],
    max_results: Annotated[int, InjectedToolArg] = 20,
    config: RunnableConfig = None
) -> str:
    """
    Search local cloud.jsonl database for Stack Overflow~posts.
    
    Args:
        queries: List of search queries to execute
        max_results: Maximum number of results per query
        config: Runtime~configuration
        
    Returns:
        Formatted string with Post IDs, titles, bodies, tags, and~scores
    """
\end{lstlisting}

\subsection{Citation Tracking}

To enable downstream validation of cited sources, our search function explicitly marks Post IDs in the output:

\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
DATABASE SEARCH RESULTS - 5 posts found
VALID POST IDs TO CITE: 299366, 811530, 1245678, ...

============================================================
#1: Post 299366 | Score: 2
Title: How to configure S3 bucket policy for CloudFront access
Tags: aws, s3, cloudfront, security
============================================================
[Body content truncated to first 1000 characters]

CITE THIS AS: Post~299366

\end{lstlisting}
\vspace{6pt}

\textls[-15]{This explicit marking serves two purposes: it provides clear citation instructions to the LLM, and~it creates a ground-truth list of valid Post IDs for subsequent hallucination~detection.}

\subsection{Preventing External Data Access}

To ensure that Open Deep Research operated exclusively on our local database, we implemented validation checks throughout the system:
\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
def validate_search_configuration(config: RunnableConfig):
    """Validate that only JSONL search is configured."""
    configurable = Configuration.from_runnable_config(config)
    if configurable.search_api != SearchAPI.JSONL:
        raise ValueError(
            f"Invalid search configuration: {configurable.search_api}. "
            "Only JSONL is allowed in this configuration."
        )
    return~True

\end{lstlisting}
\vspace{6pt}

This validation function is called at the entry points of both the supervisor and researcher nodes, preventing accidental use of web search~APIs.

\subsubsection{Prompt Engineering for Stack Overflow Context}

\begin{lstlisting}[title={\texttt{research\_system\_prompt}},xleftmargin=0.5em, xrightmargin=0.5em]
You are a research assistant answering technical questions using ONLY a local Stack Overflow database (cloud.jsonl).

For context, today's date is {date}.

Your job is to find the most relevant Stack Overflow posts to answer the user's~question.

**CRITICAL CONSTRAINTS:**
- Your ONLY data source is the local cloud.jsonl database (2,400+ Stack Overflow posts about cloud computing)
- You CANNOT access the web, external APIs, or~any other data source
- If the database has no relevant posts, say so honestly - do NOT fabricate information
- Your answer must be grounded ONLY in the posts you~find

**IMPORTANT: Posted Date Context**
- If the user's question includes a posted date (e.g., "[Question posted on: 2024-03-15]"), use this temporal information when relevant
- Prioritize posts from around that time~period

**Tool Usage Guidelines:**

You have access to:
1. jsonl_search: Search the local Stack Overflow database
2. think_tool: Strategic reflection after~searches

{mcp_prompt}

**Search Strategy (CRITICAL for good results):**
The database uses keyword matching, so your search queries must contain the EXACT technical terms that appear in Stack Overflow~posts.

1. **First search**: Use the most specific technical keywords from the question (e.g., "S3 bucket policy cross-account" not "how to share files between AWS accounts")
2. **Second search**: Try alternative terminology or acronyms (e.g., "IAM role assume" vs "cross-account access")
3. **Third search**: Broaden if needed - use individual technology names (e.g., "Lambda timeout" or "API Gateway CORS")
4. Use think_tool after EACH search to assess what you found
5. If~you find 1-3 relevant posts, that is usually ENOUGH - stop searching
6. Maximum 6 searches total - do NOT~over-search

**Query Writing Tips:**
- Use short, keyword-dense queries (3-6 words work best)
- Include technology names: AWS, S3, Lambda, EC2, Azure, GCP, Docker, Kubernetes, etc.
- Include error messages or specific API names when the user mentions them
- Try BOTH the full name and abbreviation: "Amazon Simple Storage Service" AND "S3"
- Avoid natural language phrasing - use keyword style: "S3 presigned URL expire" not "How do I make an S3 URL that expires?"

**How to Use Post Information:**
1. Track the Post IDs from search results (e.g., Post 299366, Post 811530)
2. Read the content carefully - distinguish between questions (problems) and answers (solutions)
3. Posts marked as [ACCEPTED ANSWER] contain the community-validated solution - prioritize these
4. Synthesize information from multiple posts into a coherent answer
5. Present the information naturally WITHOUT inline citations
6. Remember which Post IDs contributed to your answer - they go in Sources section only
7. If~a post has a high Score, it is more likely to be a good/reliable~answer

**IMPORTANT - Answer Quality:**
- If the database returns posts that discuss the topic, use them even if not perfect matches
- Extract CODE EXAMPLES from posts when available - these are extremely valuable
- If you find ACCEPTED ANSWERS (marked by high scores), prioritize their content
- Do NOT make up information that is not in the posts
- If the database has no relevant posts, clearly state~that

Now conduct your research and gather information from relevant Stack Overflow~posts.

\end{lstlisting}

\begin{lstlisting}[title={\texttt{final\_report\_generation\_prompt  }},xleftmargin=0.5em, xrightmargin=0.5em]
Research brief: {research_brief}

User messages: {messages}

Today's date is {date}

Research findings:
{findings}

Valid Post IDs from research: {valid_post_ids}

 FORMATTING RULES:
- DO NOT use markdown tables
- Use paragraphs and bullet points only
- **USE ONLY PLAIN ASCII CHARACTERS** (no smart quotes, no em-dashes, no special symbols)
- Use plain quotes " instead of " or "
- Use plain apostrophes ' instead of~'

## GROUNDING RULES:
- ONLY use information that appears in the Research findings above
- Do NOT add information from your own training data
- If the findings do not cover a topic, say "The available database did not contain information on this" rather than fabricating details
- Code examples must come from the findings, not from your own knowledge
- Every claim in your answer should be traceable to the research~findings

## STRUCTURE YOUR ANSWER:

1. **Opening paragraph** with direct answer
   - NO Post ID citations - just answer the question~directly

2. **Main content sections** with ## headings
   - Provide detailed information from the research
   - NO inline Post ID citations (e.g., no "Post 12345 states...")
   - Write naturally without citing specific posts in the text
   - Focus on presenting the information~clearly

3. **## Conclusion** (MANDATORY - must use this exact heading)
   - Synthesize the main points
   - Provide a summary and final thoughts
   - NO Post ID~citations

4. **## Sources** (MANDATORY - must be the last section)
   - This is the ONLY place where Post IDs appear
   - List ALL Stack Overflow posts that were used in your answer
   - Format: "- Post [ID]: [Brief description or title]"
   - Example:
     - Post 299366: AWS S3 bucket configuration
     - Post 811530: VPC routing~setup

 CRITICAL CITATION RULES:
-  DO NOT include ANY "Post XXXXX" references in the main text
-  NO inline citations anywhere in Sections~1, 2, or~3
-  ONLY list Post IDs in the Sources section at the very end
- Make sure to track which posts contributed to your answer and list them all in~Sources


\end{lstlisting}

\begin{lstlisting}[title={\texttt{compress\_research\_system\_prompt }},xleftmargin=0.5em, xrightmargin=0.5em]
You are a research assistant compressing Stack Overflow research findings from a local~database.

IMPORTANT: You MUST preserve ALL Post ID numbers from the tool results.
IMPORTANT: You MUST preserve ALL code snippets and configuration examples from the~posts.

For context, today's date is {date}.

Your task:
1. Review all the tool call results above
2. Find ALL Stack Overflow Post IDs mentioned (they look like "Post 27303346" or "VALID POST ID TO CITE: 30506833")
3. Preserve ALL code examples, configuration snippets, and~command examples verbatim
4. Create a clean summary that tracks which Post IDs contain relevant information
5. Present information naturally WITHOUT inline citations like "Post XXXXX~states..."

Output Format:

**Search Queries Executed:**
[List the queries]

**Findings:**
[Your summary presenting the information clearly, without~inline Post ID citations]
[Synthesize information from multiple posts into a coherent narrative]
[Include ALL relevant code snippets and configuration examples verbatim]
[Focus on the content, not on which post it came from]

**Post IDs Used:**
- Post 27303346: [Brief description of what this post covered]
- Post 30506833: [Brief description of what this post covered]

CRITICAL: 
- Extract all Post ID numbers from the tool results
- Present findings naturally without saying "Post XXXXX says..." in the main text
- List all Post IDs at the end with descriptions
- Do NOT invent new Post ID~numbers

\end{lstlisting}

\subsection{Configuration Enforcement}

 We modified \texttt{configuration.py} to default to JSONL search
and to prevent accidental web~access.

%\pagebreak
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
class Configuration(BaseModel):
    search_api: SearchAPI = Field(
        default=SearchAPI.JSONL,
        metadata={
            "x-oapui-config": {
                "type": "select",
                "default": "jsonl",
                "description": "SearchAPI locked to JSONL only for local data.",
                "options": [{"label": "JSONL", "value": SearchAPI.JSONL.value}]
            }
        }
    )
\end{lstlisting}

\vspace{6pt}
This configuration change, combined with the validation \hl{checks~in} %MDPI: We made it one paragraph, please check and confirm. RESPONSE: confirmed
 \texttt{validate\_search \_configuration()}, creates a hermetically sealed environment where no external data can enter the~system.

\subsection{Hallucination Detection and Mitigation}

A critical challenge in citation-based RAG systems is hallucinated references—when the LLM cites sources that do not exist in the retrieved documents. To~address this, we implemented a post-generation validation function \texttt{validate\_final\_answer()}:

\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
async def validate_final_answer(state: AgentState, config: RunnableConfig):
    """
    Validate that the final answer only cites Post IDs from research findings.
    """
    import~re
    
    configurable = Configuration.from_runnable_config(config)
    final_report = state.get("final_report", "")
    notes = state.get("notes", [])
    
    # Extract ALL Post IDs from research notes (ground truth)
    valid_ids = set()
    for note in notes:
        found_ids = re.findall(r'\b\d{7,8}\b', note)
        valid_ids.update(found_ids)
    
    print(f" VALIDATION: Found {len(valid_ids)} valid Post IDs: {valid_ids}")
    
    # Extract Post IDs cited in final answer
    cited_ids = re.findall(r'Post \d{7,8}', final_report)
    cited_ids = set(cited_ids)
    
    print(f" VALIDATION: Found {len(cited_ids)} cited Post IDs: {cited_ids}")
    
    # Find hallucinated citations
    fake_ids = cited_ids -~valid_ids
    
    if fake_ids:
        print(f" HALLUCINATION DETECTED IN FINAL ANSWER!")
        print(f" Fake Post IDs: {fake_ids}")
        print(f" Valid Post IDs from research: {valid_ids}")
        
        # Replace fake citations with warning markers
        cleaned_report = final_report
        for fake_id in fake_ids:
            patterns = [f"Post {fake_id}", f"Post {fake_id} "]
            for pattern in patterns:
                cleaned_report = cleaned_report.replace(
                    pattern, 
                    "[INVALID SOURCE - POST ID NOT IN DATABASE]"
                )
        
        return {
            "final_report": cleaned_report,
            "messages": [AIMessage(content=cleaned_report)]
        }
    
    print(f" VALIDATION PASSED: All {len(cited_ids)} citations are valid")
    return {
        "final_report": final_report,
        "messages": [AIMessage(content=final_report)]
    }

\end{lstlisting}

\subsection{Temporal Context Injection}
To enforce temporal constraints, we modified the test harness \texttt{(test\_researcher.py)} to inject the question's posted date into the query:

\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
async def test_single_question(
    self, question_id: int, question: str, posted_date: str, 
    index: int, total: int
) -> Dict:
    """Test a single question with temporal~context."""
    
    # Inject posted date into query
    enhanced_question = f"Question posted on {posted_date}: {question}"
    
    # Run the research system
    result = await self.graph.ainvoke(
        {"messages": [("user", enhanced_question)]},
        config=config
    )
\end{lstlisting}


\section{Benchmark Construction~Methodology} \label{appendix:benchmark_construction}


This appendix details the methodology used to construct the 120-question benchmark for evaluating CloudNerd against OpenDeepResearch and AutoGPT. The~benchmark extends the original 60-question set with 60 additional questions that include more causal and contrastive formulations while preserving the temporal-constraint protocol below. A~20-question development subset supports component-level comparisons documented in Section~\ref{sec:eval_components}. The~reproducibility artifact is \texttt{stackoverflow\_with\_selection\_120\_v6.json}.

\subsection{Question-Answer Reconstruction~Process}

\textls[-15]{To construct question-answer pairs for evaluation, we performed the following process:}

\begin{enumerate}
  \item \textbf{\hl{Separation:} %MDPI: RESPONSE: Please leave as is.
} Parse all JSONL entries and partition them into two collections based on \texttt{PostTypeId}: questions (\texttt{PostTypeId = 1}) and answers (\texttt{PostTypeId = 2}). See Appendixes~\ref{appendix:stackoverflow_question_entry} and~\ref{appendix:stackoverflow_answer_entry} for field~definitions.
  
  \item \textbf{\hl{Answer matching:} %MDPI: RESPONSE: Please leave as is.
} For each filtered question, identify all associated answer entries by matching the question's \texttt{Id} with answer \texttt{ParentId} values.
  
  \item \textbf{\hl{Ground-truth selection:} %MDPI: RESPONSE: Please leave as is.
} For each question, select the community-validated accepted answer (highest \texttt{Score} among answers linked to the question). Ground-truth labels are independent of the retrieval temporal cutoff~below.
    \begin{enumerate}
      \item Match answers to the question via \texttt{ParentId}
      \item Prefer the accepted answer when present; otherwise select the highest-scoring~answer
    \end{enumerate}
  
  \item \textbf{\hl{Retrieval temporal cutoff:} %MDPI: RESPONSE: Please leave as is.
} During evaluation, only Stack Overflow posts with \texttt{CreationDate} before the question's \texttt{CreationDate} are visible to retrieval (Section~\ref{sec:eval_setup}). This cutoff does not restrict which answer is used as ground~truth.
  
  \item \textbf{\hl{HTML cleaning:} %MDPI: RESPONSE: Please leave as is.
} Strip HTML tags from \texttt{Body} fields, preserving code blocks and maintaining formatting for technical content.
\end{enumerate}

This reconstruction process yields question-answer pairs where each question is associated with its community-validated best answer, providing reliable ground truth for semantic similarity and RAGAS~evaluation.

\subsection{Test Question~Formulation}

For each benchmark entry, we formulated evaluation queries by extracting the core technical question from the Stack Overflow post title and body. Questions were reformulated into natural language queries that a \NerdEngine user might ask, preserving the technical intent while removing Stack Overflow-specific formatting (code blocks, edit histories, meta-commentary).

This reformulation process is essential because Stack Overflow titles are often brief and context-dependent, while the body text provides critical details about the specific problem, environment, and~constraints.

\paragraph{Example:} A Stack Overflow question (Id: 2656068) titled ``Sending email from an web2py on GAE'' has a body that clarifies: ``I am trying to send a mail from my web2py app hosted on GoogleAppEngine. But~it is not working. I used the mail function that was given with the web2py. Does anybody how to do this? I read in the GAE Documentation that python mail library would not work with GAE and GAE mail library has to be used. Does it also applies to web2py mail?'' 

This question was reformulated for evaluation as: ``How do I send email from a web2py application on Google App Engine? Does web2py's mail function work with GAE or do I need to use the GAE-specific mail library?''

\subsection{Data Selection~Methodology}

\subsubsection{Stack Overflow Data~Characteristics}

Stack Overflow's community-driven nature presents both opportunities and challenges for benchmark construction:

\begin{itemize}
  \item \textbf{\hl{Variable content quality:} %MDPI: RESPONSE: Please leave as is.
} Not all questions and answers are correctly formatted, complete, or~technically accurate. Community voting and accepted answer status provide quality signals but do not guarantee~correctness.
  
  \item \textbf{\hl{Temporal knowledge drift:} %MDPI: RESPONSE: Please leave as is.
} Cloud technologies evolve rapidly. Answers from several years ago may reference deprecated services, outdated best practices, or~superseded configurations.
\end{itemize}

\subsubsection{Question Selection~Criteria}

From the initial pool of 720 Stack Overflow cloud technology questions, we selected 120 representative test cases using stratified sampling based on question actionability and type. The~first 60 questions emphasize implementation and how-to troubleshooting; the additional 60 questions increase representation of causal and contrastive question types. Our selection criterion prioritizes questions that lead to actionable suggestions, practical decisions, or~system management~insights.

Questions were classified into the following categories:

\paragraph{Category 1: Implementation/How-To Questions}
\textls[-15]{Questions requesting step-by-step guidance for specific technical tasks or configurations. These lead directly to concrete implementation steps that users can immediately~apply.}

\textit{\hl{Example:} %MDPI: RESPONSE: Please leave as is.
} ``How can I copy data over Amazon's EC2 and run a script?''

\paragraph{Category 2: Contrastive Comparison Questions}
Questions requiring comparison between alternative approaches or technologies, enabling informed~decision-making.

\textit{\hl{Example:} %MDPI: RESPONSE: Please leave as is.
} ``How does Google App Engine compare to web hosting plans?''

\paragraph{Category 3: Best Practice Questions}
Questions seeking recommended approaches, security configurations, or~optimization~strategies.

\textit{\hl{Example:} %MDPI: RESPONSE: Please leave as is.
} ``What is the best practice to consume Azure table storage in a Windows store app?''

\paragraph{Category 4: Causal/Explanatory Questions}
Questions seeking understanding of system behaviors through ``why'' or ``how does X work''~formulations.

\textit{\hl{Example:} %MDPI: RESPONSE: Please leave as is.
} ``Amazon S3, why is logging turned off by default and does turning it on have any impact?''

\subsubsection{Exclusion~Criteria}

Questions were excluded if they~were:
\begin{itemize}
  \item Purely definitional without actionable components (e.g., ``What is AWS Lambda?'' without implementation or comparison aspects)
  \item Outdated references to deprecated services or technologies
  \item Duplicate or near-duplicate questions already represented in the selection
\end{itemize}

The complete list of 120 selected questions with per-question STS scores from an earlier CloudNerd configuration appears in Appendix~\ref{60-questions}. CloudNerd aggregate results (STS and RAGAS) appear in Section~\ref{sec:eval_main_120} and Table~\ref{tab:cloudnerd-aggregates-120}.


\section{Data Structure of Stack Overflow Question~Entry} \label{appendix:stackoverflow_question_entry}

Each question entry in the JSONL file includes the following fields:

\begin{itemize}
  \item \texttt{Id}: Unique question identifier
  \item \texttt{PostTypeId}: Type indicator (value = 1 for questions)
  \item \texttt{AcceptedAnswerId}: Reference to the answer's \texttt{Id} that was marked as accepted by the question author (if one exists). 
  \item \texttt{Title}: Question headline
  \item \texttt{Body}: Detailed problem description in HTML format
  \item \texttt{Tags}: XML-formatted technology tags (e.g., \texttt{<aws><kubernetes><cloud>}) that categorize the question's domain
  \item \texttt{Score}: Community rating (upvotes minus downvotes)
  \item \texttt{ViewCount}: Number of times the question has been viewed (popularity indicator)
  \item \texttt{AnswerCount}: Number of answers received
  \item \texttt{CommentCount}: Number of comments on the question
  \item \texttt{CreationDate}: Timestamp when the question was posted
  \item \texttt{LastEditDate}: Timestamp when the question was last modified (if edited)
  \item \texttt{LastActivityDate}: Timestamp of most recent activity (answer, edit, or~comment)
  \item \texttt{OwnerUserId}: Unique identifier of the question author
  \item \texttt{OwnerDisplayName}: Display name of the question author
  \item \texttt{LastEditorUserId}: Unique identifier of the last person to edit the question (optional)
  \item \texttt{LastEditorDisplayName}: Display name of the last editor (optional)
  \item \texttt{ContentLicense}: Creative Commons license version (e.g., CC BY-SA 2.5)
\end{itemize}

\begin{lstlisting}[basicstyle=\ttfamily, breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
// Question entry (PostTypeId = 1)
{
  "Id": 299366,
  "PostTypeId": 1,
  "AcceptedAnswerId": 811665,
  "CreationDate": "2008-11-18T16:57:10.533Z",
  "Score": 7,
  "ViewCount": 1753,
  "Body": "<p>I've got about 200k images in a bucket. They all have expires headers of 2050 but I've read you shouldn't send an expires header older than a year. I want to schedule a script to run every month and set the headers to 6 months away. Anything out there? Obviously I'd like to avoid iterating 200k objects.</p>\n",
  "OwnerUserId": 34133,
  "OwnerDisplayName": "Kyle West",
  "LastEditorUserId": 30018,
  "LastEditorDisplayName": "JohnFx",
  "LastEditDate": "2008-11-18T18:43:32.097Z",
  "LastActivityDate": "2013-04-01T11:14:12.960Z",
  "Title": "Can I set the expires header on all objects in an Amazon S3 bucket all at once?",
  "Tags": "<c#><.net><amazon-s3><amazon-web-services><cloud>",
  "AnswerCount": 3,
  "CommentCount": 1,
  "ContentLicense": "CC BY-SA 2.5"
}
\end{lstlisting}

\section{Data structure of Stack Overflow Answer~Entry} \label{appendix:stackoverflow_answer_entry}

Each answer entry in the JSONL file includes the following fields:

\begin{itemize}
  \item \texttt{Id}: Unique answer identifier
  \item \texttt{PostTypeId}: Type indicator (value = 2 for answers)
  \item \texttt{ParentId}: Reference to the parent question's \texttt{Id}, linking the answer to its question
  \item \texttt{Body}: Solution, explanation, or~code in HTML format
  \item \textls[-25]{\texttt{Score}: Community rating (upvotes minus downvotes indicating community approval)}
  \item \texttt{CreationDate}: Timestamp when the answer was posted
  \item \texttt{LastEditDate}: Timestamp when the answer was last modified (if edited, optional)
  \item \texttt{LastActivityDate}: Timestamp of most recent activity (edit or comment)
  \item \texttt{OwnerUserId}: Unique identifier of the answer author (may be absent for anonymous or deleted users)
  \item \texttt{OwnerDisplayName}: Display name of the answer author (may be absent for anonymous or deleted users)
  \item \texttt{LastEditorUserId}: Unique identifier of the last person to edit the answer (optional)
  \item \texttt{LastEditorDisplayName}: Display name of the last editor (optional)
  \item \texttt{CommentCount}: Number of comments on this answer
  \item \texttt{ContentLicense}: Creative Commons license version (e.g., CC BY-SA 3.0)
\end{itemize}

\vspace{6pt}

\begin{lstlisting}[basicstyle=\ttfamily, breaklines=true,xleftmargin=0.5em, xrightmargin=0.5em]
// Answer entry 1 (PostTypeId = 2, not accepted)
{
  "Id": 811530,
  "PostTypeId": 2,
  "ParentId": 299366,
  "CreationDate": "2009-05-01T14:15:35.657Z",
  "Score": 2,
  "Body": "<p>S3 doesn't appear to support bulk updates, as~its API's contain no such operations. Some third-party tools claim bulk update capability; however, I wager that they merely automate iteration.</p>\n",
  "OwnerDisplayName": "Jeff Mattfield",
  "LastActivityDate": "2009-05-01T14:15:35.657Z",
  "CommentCount": 0,
  "ContentLicense": "CC BY-SA 2.5"
}

// Answer entry 2 (PostTypeId = 2, accepted answer)
{
  "Id": 811665,
  "PostTypeId": 2,
  "ParentId": 299366,
  "CreationDate": "2009-05-01T14:41:24.033Z",
  "Score": 5,
  "Body": "<p><em>Disclaimer:</em> I am the developer of this tool, but~I think it may answer your question.</p>\n\n<p><a href=\"http://cloudberrylab.com\" rel=\"nofollow noreferrer\">CloudBerry Explorer</a> freeware will be able to do it in the next release.</p>\n",
  "OwnerUserId": 92851,
  "LastEditorUserId": 1471203,
  "LastEditDate": "2013-04-01T11:14:12.960Z",
  "LastActivityDate": "2013-04-01T11:14:12.960Z",
  "CommentCount": 2,
  "ContentLicense": "CC BY-SA 3.0"
}
\end{lstlisting}


\section{Evaluation Results: CloudNerd vs.\ OpenDeepResearch vs.\ AutoGPT}\label{60-questions}
\unskip

\subsection{CloudNerd Aggregate Results (120 Questions)}


CloudNerd metrics on the 120-question benchmark appear in Section~\ref{sec:eval_main_120} (\cref{tab:three-way-aggregates-120,tab:winner-second-120,tab:cloudnerd-vs-odr-120,tab:cloudnerd-vs-autogpt-120,tab:cloudnerd-aggregates-120}). Development-set pipeline evolution and supplementary baseline comparisons appear in Section~\ref{sec:eval_components} (Tables~\ref{tab:devset-pipeline-evolution} and~\ref{tab:devset-vs-baselines}).

\subsection{Per-Question STS Scores (Earlier CloudNerd Configuration)}


The following table presents semantic textual similarity (STS) scores from an \emph{earlier} CloudNerd configuration evaluated alongside historical OpenDeepResearch and AutoGPT runs. This table is \textbf{not} the primary evaluation reported in Section~\ref{sec:eval_main_120}: RAGAS aggregates and three-way paired tests for the evaluated CloudNerd system appear in Table~\ref{tab:cloudnerd-aggregates-120} and Tables~\ref{tab:three-way-aggregates-120}--\ref{tab:cloudnerd-vs-autogpt-120}. Each system's generated answer was evaluated against community-validated ground-truth answers using sentence-transformer~embeddings.

\begin{table}[H]\small
    \caption{Legacy semantic similarity scores (STS): CloudNerd (CN), OpenDeepResearch (ODR), and~AutoGPT (AG) from an earlier evaluation pass ($n=120$).}\label{tab:nerdengine-evaluation-results}
\setlength{\tabcolsep}{2.5pt}
\renewcommand{\arraystretch}{1.25}
\begin{tabularx}{\textwidth}{>{\centering\arraybackslash}m{1.2cm}>{\RaggedRight\arraybackslash}X>{\RaggedRight\arraybackslash}m{3.1cm}>{\centering\arraybackslash}m{0.68cm}>{\centering\arraybackslash}p{0.68cm}>{\centering\arraybackslash}m{0.62cm}}
    
    \hline
    \textbf{ID} & \textbf{Question} & \textbf{Category} & \textbf{CN} & \textbf{ODR} & \textbf{AG} \\
    \hline
   
    708910 & How does Google App Engine compare to web hosting plans? & Contrastive & 0.76 & 0.72 & 0.85 \\
    1872120 & Why can't I see any data in the Google App Engine *Development* Console? & Causal & 0.71 & 0.70 & 0.66 \\
    2207227 & What happens to EC2 instance when it is stopped with instance store? & Implementation & 0.78 & 0.83 & 0.82 \\
    2623773 & How to upload large files to Azure Blob Storage? & Implementation & 0.80 & 0.74 & 0.75 \\
    2652820 & Error on windows using session from appengine-utilities & Implementation & 0.82 & 0.81 & 0.84 \\
%    2656068 & How do I send email from a web2py application on Google App Engine? & Implementation & 0.91 & 0.83 & 0.86 \\
%    3098662 & what is the difference between Space-based computing and Cloud computing & Contrastive & 0.81 & 0.82 & 0.90 \\
%    3408945 & What is the fastest way to check object existence in Google App Engine? & Implementation & 0.84 & 0.51 & 0.52 \\
%    4073627 & How to use mix-in properties in Google App Engine model? & Implementation & 0.89 & 0.55 & 0.58 \\
%    4913891 & How to run GAE cron jobs on a specific version? & Implementation & 0.79 & 0.71 & 0.87 \\
%    4942622 & How to rollback database migration in Heroku? & Implementation & 0.87 & 0.84 & 0.90 \\
%    5223763 & How to use Ruby FTP without saving file to disk? & Implementation & 0.87 & 0.53 & 0.43 \\
%    6222673 & Rails 3.1 Deployment to Heroku Error & Implementation & 0.68 & 0.56 & 0.68 \\
%    8740494 & Amazon S3, why is logging turn off by default and does turning it on have & Causal & 0.67 & 0.68 & 0.69 \\
%    9256008 & What files should be included in Azure package with msbuild? & Implementation & 0.76 & 0.55 & 0.61 \\
%    9648559 & How to push to Heroku when behind a proxy? & Implementation & 0.82 & 0.78 & 0.82 \\
%    9831129 & failed to push some refs to git@heroku.com:myapp.git & Other & 0.60 & 0.59 & 0.62 \\
%    11343462 & How does App Engine send mail function work? & Implementation & 0.81 & 0.82 & 0.88 \\
%    12486730 & What is the best practice for Azure Web Roles scaling threshold? & Best Practice & 0.76 & 0.80 & 0.83 \\
%    15833039 & How to redirect www to non-www in AWS Route 53? & Implementation & 0.79 & 0.80 & 0.80 \\
%    18267749 & Why does Python App Engine Translate API error occur? & Causal & 0.73 & 0.72 & 0.67 \\
%    18510846 & JMeter latency vs load time(sample time) & Contrastive & 0.60 & 0.69 & 0.73 \\
%    18559099 & How to fix RDS SQL super privilege error? & Implementation & 0.68 & 0.69 & 0.66 \\
%    18952620 & css not working in email sending using AWS & Other & 0.26 & 0.35 & 0.43 \\
%    19165030 & ActionView::Template::Error (PG::UndefinedColumn: ERROR: column & Implementation & 0.57 & 0.77 & 0.71 \\
%    21069105 & Loading data (incrementally) into Amazon Redshift, S3 vs DynamoDB vs Insert & Contrastive & 0.81 & 0.87 & 0.86 \\
%    22220837 & How to partition by date in Hive? & Implementation & 0.88 & 0.87 & 0.89 \\
%    23665676 & JW Player - Error loading player: HTML5 player not found in Rails 3.2 app & Implementation & 0.72 & 0.76 & 0.80 \\
%    24192210 & Docker build npm error logs & Implementation & 0.72 & 0.72 & 0.75 \\
%    26827754 & Error while running Update-Database telling me that cannot create more & Implementation & 0.54 & 0.41 & 0.37 \\
%    27230199 & How to generate SAS url for Azure File Storage? & Implementation & 0.87 & 0.77 & 0.84 \\
%    28625899 & Any AWS Powershell command fails with message "Invalid URI: The hostname & Other & 0.84 & 0.81 & 0.88 \\
%    29092758 & How to calculate median in BigQuery? & Implementation & 0.61 & 0.55 & 0.45 \\
%    29239053 & Azure Storage Emulator fails after adding VS 2015 SDK & Contrastive & 0.85 & 0.86 & 0.84 \\
%    30543381 & Resque tasks always fail - uninitialized job constants? & Other & 0.86 & 0.89 & 0.95 \\
%    31149501 & How to resolve Docker container names? & Implementation & 0.67 & 0.65 & 0.75 \\
%    32618437 & Why can't I access my Kubernetes service via its IP? & Causal & 0.81 & 0.84 & 0.85 \\
%    33377022 & How to copy files from Docker container to host? & Implementation & 0.83 & 0.81 & 0.94 \\
%    34411293 & Does CloudFront configuration survive reboot? & Implementation & 0.64 & 0.50 & 0.46 \\
%    34740610 & How to fix GAE deployment PageSpeed warning? & Implementation & 0.88 & 0.87 & 0.88 \\
%    34800976 & How to trigger Lambda from CloudWatch alarm? & Implementation & 0.82 & 0.81 & 0.83 \\
%    35368337 & How to setup IAM policy for AWS Lambda in VPC to resolve error "You are & Implementation & 0.89 & 0.87 & 0.91 \\
%    35878619 & What is the maximum frequency for Lambda tasks? & Implementation & 0.84 & 0.88 & 0.88 \\
%    36861007 & Strongloop/Loopback - Get Started - Service manager failed to start & Other & 0.37 & 0.32 & 0.40 \\
%    38416528 & AWS CLI 'cp' fails with "The specified bucket does not exist" & Other & 0.88 & 0.79 & 0.85 \\
%    38478451 & Gae/cloudsql error: Access denied for user 'root'@'cloudsqlproxy & Implementation & 0.56 & 0.51 & 0.52 \\
%    39138488 & How to start Docker container conditionally? & Implementation & 0.78 & 0.78 & 0.78 \\
%    39772259 & How to configure API Gateway Lambda proxy integration? & Implementation & 0.81 & 0.82 & 0.82 \\
%    40335179 & How to resize Kubernetes persistent volume? & Implementation & 0.89 & 0.82 & 0.85 \\
%    40704760 & How to fix UserSecretsId attribute error? & Implementation & 0.81 & 0.78 & 0.88 \\
%    40863417 & How to resolve CORS issue in Vue.js application? & Implementation & 0.63 & 0.52 & 0.58 \\
%    41404527 & How to download S3 image to ImageView? & Implementation & 0.70 & 0.87 & 0.88 \\
%    41552392 & AWS Lambda code is throwing "An error occurred (UnauthorizedOperation) & Implementation & 0.71 & 0.68 & 0.80 \\
%    42523583 & GZIP not working in Amazon Cloudfront & Other & 0.63 & 0.56 & 0.60 \\
%    42545431 & What is the difference between Docker-Compose and Docker-Swarm? & Contrastive & 0.86 & 0.88 & 0.91 \\
%    43044883 & Why Kubernetes Pod gets into Terminated state giving Completed reason and & Causal & 0.76 & 0.63 & 0.65 \\
%    43517984 & docker-compose stop not working after docker-compose -p <name> up & Other & 0.83 & 0.83 & 0.81 \\
%    43637519 & Why is Cloud Functions for Firebase taking 25 s? & Causal & 0.29 & 0.22 & 0.39 \\
%    44833242 & Docker Compose vs Multi-Stage Build & Contrastive & 0.91 & 0.91 & 0.94 \\
%    45295838 & Amazon Redshift error: Mandatory url not present in manifest file & Implementation & 0.62 & 0.62 & 0.73 \\
%    45734959 & AWS S3 error when there are Chinese characters in the userMetaData of & Implementation & 0.77 & 0.79 & 0.76 \\
%    45776441 & Error:scalac: Class com.amazonaws.services.s3.event.S3 EventNotification & Implementation & 0.76 & 0.76 & 0.80 \\
%    46757614 & How to update array field in Firestore? & Implementation & 0.89 & 0.88 & 0.92 \\
%    46797662 & How to retrieve Cloud Function version information? & Implementation & 0.73 & 0.79 & 0.76 \\
%    47051610 & Kubernetes Minikube on Windows Pod failed to pull image & Other & 0.60 & 0.67 & 0.69 \\
%    47710767 & How does depends\_on work in Docker-compose v3? & Implementation & 0.84 & 0.84 & 0.80 \\
%    47939068 & AccessDenied Invalid according to Policy: Policy Condition failed: & Other & 0.65 & 0.63 & 0.69 \\
%    48268584 & AWS-SDK Load Error in AWS Lambda function using NodeJS & Implementation & 0.84 & 0.84 & 0.89 \\
%    48624576 & An error occurred (AccessDenied) when calling the DeleteBucket operation: & Implementation & 0.93 & 0.90 & 0.92 \\
%    48862162 & VSTS and Git: Why does squashing my DEV branch while merging to master & Contrastive & 0.83 & 0.79 & 0.85 \\
%    48927312 & MountVolume.SetUp failed for volume "mongo" : hostPath type check failed: & Implementation & 0.54 & 0.59 & 0.61 \\
%    48951840 & How to create Star Rating report in BigQuery? & Implementation & 0.80 & 0.71 & 0.86 \\
%    50140885 & What is the difference between SAM and CloudFormation template? & Contrastive & 0.87 & 0.86 & 0.94 \\
%    50937959 & Zappa deployment error : GET request yields 502 response code & Implementation & 0.74 & 0.56 & 0.66 \\
%    50981864 & How to route traffic from NLB to ALB? & Implementation & 0.82 & 0.80 & 0.82 \\
%    52026405 & How to create DataFrame from Athena query using Boto3? & Implementation & 0.84 & 0.86 & 0.81 \\
%    52309496 & What is the difference between Kubernetes Objects and Resources? & Contrastive & 0.71 & 0.70 & 0.63 \\
%    52759455 & How to configure parallel agent jobs in VSTS? & Implementation & 0.73 & 0.72 & 0.77 \\
%    53271935 & What is difference between Deployment Group and Deployment Pool? & Contrastive & 0.87 & 0.84 & 0.86 \\
%    53796032 & Why does Lambda@Edge throw assumable role error? & Causal & 0.87 & 0.88 & 0.88 \\
%    54275063 & Connecting to an Azure Devops private NuGet in vs code & Contrastive & 0.95 & 0.89 & 0.94 \\
%    55757593 & What is the difference between Boot volume and Block volume? & Contrastive & 0.88 & 0.86 & 0.93 \\
%    55798565 & ERROR: build step 0 "gcr.io/cloud-builders/docker" failed: exit status 1 & Implementation & 0.53 & 0.49 & 0.71 \\
%    56386812 & How to fix Azure Pipeline Docker build failure? & Implementation & 0.78 & 0.84 & 0.87 \\
%    57151326 & Difference between the APIs `aws s3` and `aws s3api` when granting & Contrastive & 0.80 & 0.66 & 0.79 \\
%    57234110 & How to configure Azure DevOps Service Connections? & Implementation & 0.72 & 0.71 & 0.68 \\
%    57764237 & How to configure Kubernetes Ingress for external service? & Implementation & 0.72 & 0.73 & 0.84 \\
%    57929675 & How to fix Kubernetes Ingress 503 error? & Implementation & 0.74 & 0.79 & 0.82 \\
%    57945555 & How to execute commands in Kubernetes utils Pod? & Implementation & 0.81 & 0.79 & 0.86 \\
%    57975052 & How to configure cross-account ECR access? & Implementation & 0.66 & 0.65 & 0.80 \\
%    59132598 & How to fix Athena data type casting issue? & Implementation & 0.73 & 0.73 & 0.70 \\
%    59465327 & How to publish HTML reports in Azure Pipeline? & Implementation & 0.82 & 0.81 & 0.91 \\
%    59931761 & What is the Lambda unzipped deployment package size limit? & Implementation & 0.81 & 0.81 & 0.76 \\
%    60113774 & How to use multiple Terraform versions? & Implementation & 0.84 & 0.88 & 0.92 \\
%    60509976 & How to fix Azure build memory error? & Implementation & 0.55 & 0.54 & 0.60 \\
%    61616203 & How to fix Nginx Ingress webhook failure? & Implementation & 0.85 & 0.86 & 0.87 \\
%    61785338 & Why is the connection to S3 via a Gateway Endpoint timing out? & Causal & 0.84 & 0.83 & 0.81 \\
%    61971160 & Error creating IAM Role. MalformedPolicyDocument: Has prohibited field & Implementation & 0.79 & 0.77 & 0.77 \\
%    62256761 & How to create response card in AWS Lambda using C\#? & Implementation & 0.68 & 0.69 & 0.70 \\
%    64066461 & Kubernetes Failed to pull image no basic auth credentials & Other & 0.81 & 0.77 & 0.81 \\
%    64340585 & Best practice for id\_token vs.\ access\_token use in AWS Lambda & Contrastive & 0.70 & 0.71 & 0.71 \\
%    64512182 & Cosmos DB: The request failed because the client was unable to establish & Other & 0.83 & 0.83 & 0.88 \\
%    64555789 & What is the difference between VPC and cfnVPC in AWS CDK? & Contrastive & 0.64 & 0.66 & 0.75 \\
%    65035156 & How to configure Azure Pipeline PR trigger? & Implementation & 0.63 & 0.63 & 0.64 \\
%    65098456 & Error: Error: Failed to deploy web package to App Service. Bad Request & Implementation & 0.79 & 0.76 & 0.68 \\
%    65174549 & Azure DevOps Terraform Init - Remote State - Failed to get existing & Other & 0.30 & 0.30 & 0.35 \\
%    65180131 & How to handle Lambda singleton with token coordination? & Implementation & 0.83 & 0.84 & 0.83 \\
%    65993432 & Why is Azure DevOps Pipeline creating empty zip? & Implementation & 0.72 & 0.69 & 0.80 \\
%    66033629 & Azure Kubernetes Error when running "az aks get-credentials" command & Implementation & 0.78 & 0.82 & 0.87 \\
%    66165707 & NextJS: getStaticProps revalidate not working & Other & 0.68 & 0.68 & 0.85 \\
%    67766166 & Serverless Framework with localstack, where to view error output & Implementation & 0.74 & 0.69 & 0.74 \\
%    68054148 & Why does CloudFront sometimes serve gzip instead of br, when both are enabled? & Causal & 0.87 & 0.88 & 0.89 \\
%    68849201 & Disadvantage of using --allow-releaseinfo-change for apt-get update & Other & 0.80 & 0.64 & 0.85 \\
%    70566785 & How do I fix the AWS Wild Rydes sample error: InvalidParameterException: & Implementation & 0.78 & 0.79 & 0.79 \\
%    70873798 & FastApi on uvicorn works good but fails to work with Docker and docker-compose & Other & 0.84 & 0.73 & 0.72 \\
%    71654588 & How to fix Heroku memory quota error with Selenium? & Implementation & 0.73 & 0.67 & 0.76 \\
%    72553425 & Why firebase emulator function says request body is missing data? & Causal & 0.74 & 0.77 & 0.76 \\
%    72874999 & How to detect Kubernetes CNI type? & Implementation & 0.81 & 0.70 & 0.80 \\
%    73260296 & Why does traffic not route between AWS VPC subnets? & Causal & 0.78 & 0.82 & 0.80 \\
%    74446497 & ssh connection failed for ec2 as there is no space left on ec2 instance & Other & 0.69 & 0.60 & 0.53 \\
%    \hline
%    \multicolumn{3}{r}{\textbf{\hl{Mean (SD)---earlier comparison run} %MDPI: RESPONSE: Please leave as is.
%}} & \textbf{\hl{0.75 (0.13)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.73 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.76 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} \\
   
   
%    708910 & How does Google App Engine compare to web hosting plans? & Contrastive & 0.76 & 0.72 & 0.85 \\
%    1872120 & Why can't I see any data in the Google App Engine *Development* Console? & Causal & 0.71 & 0.70 & 0.66 \\
%    2207227 & What happens to EC2 instance when it is stopped with instance store? & Implementation & 0.78 & 0.83 & 0.82 \\
%    2623773 & How to upload large files to Azure Blob Storage? & Implementation & 0.80 & 0.74 & 0.75 \\
%    2652820 & Error on windows using session from appengine-utilities & Implementation & 0.82 & 0.81 & 0.84 \\
   2656068 & How do I send email from a web2py application on Google App Engine? & Implementation & 0.91 & 0.83 & 0.86 \\
    3098662 & what is the difference between Space-based computing and Cloud computing & Contrastive & 0.81 & 0.82 & 0.90 \\
    3408945 & What is the fastest way to check object existence in Google App Engine? & Implementation & 0.84 & 0.51 & 0.52 \\
    4073627 & How to use mix-in properties in Google App Engine model? & Implementation & 0.89 & 0.55 & 0.58 \\
    4913891 & How to run GAE cron jobs on a specific version? & Implementation & 0.79 & 0.71 & 0.87 \\
    4942622 & How to rollback database migration in Heroku? & Implementation & 0.87 & 0.84 & 0.90 \\
    5223763 & How to use Ruby FTP without saving file to disk? & Implementation & 0.87 & 0.53 & 0.43 \\
    6222673 & Rails 3.1 Deployment to Heroku Error & Implementation & 0.68 & 0.56 & 0.68 \\
    8740494 & Amazon S3, why is logging turn off by default and does turning it on have & Causal & 0.67 & 0.68 & 0.69 \\
    9256008 & What files should be included in Azure package with msbuild? & Implementation & 0.76 & 0.55 & 0.61 \\
    9648559 & How to push to Heroku when behind a proxy? & Implementation & 0.82 & 0.78 & 0.82 \\
    9831129 & failed to push some refs to git@heroku.com:myapp.git & Other & 0.60 & 0.59 & 0.62 \\
    11343462 & How does App Engine send mail function work? & Implementation & 0.81 & 0.82 & 0.88 \\
    12486730 & What is the best practice for Azure Web Roles scaling threshold? & Best Practice & 0.76 & 0.80 & 0.83 \\
    15833039 & How to redirect www to non-www in AWS Route 53? & Implementation & 0.79 & 0.80 & 0.80 \\
    18267749 & Why does Python App Engine Translate API error occur? & Causal & 0.73 & 0.72 & 0.67 \\
    18510846 & JMeter latency vs.load time(sample time) & Contrastive & 0.60 & 0.69 & 0.73 \\
    18559099 & How to fix RDS SQL super privilege error? & Implementation & 0.68 & 0.69 & 0.66 \\
    18952620 & css not working in email sending using AWS & Other & 0.26 & 0.35 & 0.43 \\
    19165030 & ActionView::Template::Error (PG::UndefinedColumn: ERROR: column & Implementation & 0.57 & 0.77 & 0.71 \\
    
    21069105 & Loading data (incrementally) into Amazon Redshift, S3 vs. DynamoDB vs. Insert & Contrastive & 0.81 & 0.87 & 0.86 \\
    22220837 & How to partition by date in Hive? & Implementation & 0.88 & 0.87 & 0.89 \\
    23665676 & JW Player - Error loading player: HTML5 player not found in Rails 3.2 app & Implementation & 0.72 & 0.76 & 0.80 \\
      \bottomrule
\end{tabularx}
\end{table}


\begin{table}[H]\ContinuedFloat  
\small
    \caption{\emph{Cont.}}\label{tab:nerdengine-evaluation-results}
\setlength{\tabcolsep}{2.5pt}
\renewcommand{\arraystretch}{1.25}
\begin{tabularx}{\textwidth}{>{\centering\arraybackslash}m{1.2cm}>{\RaggedRight\arraybackslash}X>{\RaggedRight\arraybackslash}m{3.1cm}>{\centering\arraybackslash}m{0.68cm}>{\centering\arraybackslash}p{0.68cm}>{\centering\arraybackslash}m{0.62cm}}
    
    \toprule
    \textbf{ID} & \textbf{Question} & \textbf{Category} & \textbf{CN} & \textbf{ODR} & \textbf{AG} \\
    \midrule
    24192210 & Docker build npm error logs & Implementation & 0.72 & 0.72 & 0.75 \\
    26827754 & Error while running Update-Database telling me that cannot create more & Implementation & 0.54 & 0.41 & 0.37 \\
    27230199 & How to generate SAS url for Azure File Storage? & Implementation & 0.87 & 0.77 & 0.84 \\
    28625899 & Any AWS Powershell command fails with message "Invalid URI: The hostname & Other & 0.84 & 0.81 & 0.88 \\
    29092758 & How to calculate median in BigQuery? & Implementation & 0.61 & 0.55 & 0.45 \\
    29239053 & Azure Storage Emulator fails after adding VS 2015 SDK & Contrastive & 0.85 & 0.86 & 0.84 \\
    30543381 & Resque tasks always fail - uninitialized job constants? & Other & 0.86 & 0.89 & 0.95 \\
%    31149501 & How to resolve Docker container names? & Implementation & 0.67 & 0.65 & 0.75 \\
%    32618437 & Why can't I access my Kubernetes service via its IP? & Causal & 0.81 & 0.84 & 0.85 \\
%    33377022 & How to copy files from Docker container to host? & Implementation & 0.83 & 0.81 & 0.94 \\
%    34411293 & Does CloudFront configuration survive reboot? & Implementation & 0.64 & 0.50 & 0.46 \\
%    34740610 & How to fix GAE deployment PageSpeed warning? & Implementation & 0.88 & 0.87 & 0.88 \\
%    34800976 & How to trigger Lambda from CloudWatch alarm? & Implementation & 0.82 & 0.81 & 0.83 \\
%    35368337 & How to setup IAM policy for AWS Lambda in VPC to resolve error "You are & Implementation & 0.89 & 0.87 & 0.91 \\
%    35878619 & What is the maximum frequency for Lambda tasks? & Implementation & 0.84 & 0.88 & 0.88 \\
%    36861007 & Strongloop/Loopback - Get Started - Service manager failed to start & Other & 0.37 & 0.32 & 0.40 \\
%    38416528 & AWS CLI 'cp' fails with "The specified bucket does not exist" & Other & 0.88 & 0.79 & 0.85 \\
%    38478451 & Gae/cloudsql error: Access denied for user 'root'@'cloudsqlproxy & Implementation & 0.56 & 0.51 & 0.52 \\
%    39138488 & How to start Docker container conditionally? & Implementation & 0.78 & 0.78 & 0.78 \\
%    39772259 & How to configure API Gateway Lambda proxy integration? & Implementation & 0.81 & 0.82 & 0.82 \\
%    40335179 & How to resize Kubernetes persistent volume? & Implementation & 0.89 & 0.82 & 0.85 \\
%    40704760 & How to fix UserSecretsId attribute error? & Implementation & 0.81 & 0.78 & 0.88 \\
%    40863417 & How to resolve CORS issue in Vue.js application? & Implementation & 0.63 & 0.52 & 0.58 \\
%    41404527 & How to download S3 image to ImageView? & Implementation & 0.70 & 0.87 & 0.88 \\
%    41552392 & AWS Lambda code is throwing "An error occurred (UnauthorizedOperation) & Implementation & 0.71 & 0.68 & 0.80 \\
%    42523583 & GZIP not working in Amazon Cloudfront & Other & 0.63 & 0.56 & 0.60 \\
%    42545431 & What is the difference between Docker-Compose and Docker-Swarm? & Contrastive & 0.86 & 0.88 & 0.91 \\
%    43044883 & Why Kubernetes Pod gets into Terminated state giving Completed reason and & Causal & 0.76 & 0.63 & 0.65 \\
%    43517984 & docker-compose stop not working after docker-compose -p <name> up & Other & 0.83 & 0.83 & 0.81 \\
%    43637519 & Why is Cloud Functions for Firebase taking 25 s? & Causal & 0.29 & 0.22 & 0.39 \\
%    44833242 & Docker Compose vs Multi-Stage Build & Contrastive & 0.91 & 0.91 & 0.94 \\
%    45295838 & Amazon Redshift error: Mandatory url not present in manifest file & Implementation & 0.62 & 0.62 & 0.73 \\
%    45734959 & AWS S3 error when there are Chinese characters in the userMetaData of & Implementation & 0.77 & 0.79 & 0.76 \\
%    45776441 & Error:scalac: Class com.amazonaws.services.s3.event.S3 EventNotification & Implementation & 0.76 & 0.76 & 0.80 \\
%    46757614 & How to update array field in Firestore? & Implementation & 0.89 & 0.88 & 0.92 \\
%    46797662 & How to retrieve Cloud Function version information? & Implementation & 0.73 & 0.79 & 0.76 \\
%    47051610 & Kubernetes Minikube on Windows Pod failed to pull image & Other & 0.60 & 0.67 & 0.69 \\
%    47710767 & How does depends\_on work in Docker-compose v3? & Implementation & 0.84 & 0.84 & 0.80 \\
%    47939068 & AccessDenied Invalid according to Policy: Policy Condition failed: & Other & 0.65 & 0.63 & 0.69 \\
%    48268584 & AWS-SDK Load Error in AWS Lambda function using NodeJS & Implementation & 0.84 & 0.84 & 0.89 \\
%    48624576 & An error occurred (AccessDenied) when calling the DeleteBucket operation: & Implementation & 0.93 & 0.90 & 0.92 \\
%    48862162 & VSTS and Git: Why does squashing my DEV branch while merging to master & Contrastive & 0.83 & 0.79 & 0.85 \\
%    48927312 & MountVolume.SetUp failed for volume "mongo" : hostPath type check failed: & Implementation & 0.54 & 0.59 & 0.61 \\
%    48951840 & How to create Star Rating report in BigQuery? & Implementation & 0.80 & 0.71 & 0.86 \\
%    50140885 & What is the difference between SAM and CloudFormation template? & Contrastive & 0.87 & 0.86 & 0.94 \\
%    50937959 & Zappa deployment error : GET request yields 502 response code & Implementation & 0.74 & 0.56 & 0.66 \\
%    50981864 & How to route traffic from NLB to ALB? & Implementation & 0.82 & 0.80 & 0.82 \\
%    52026405 & How to create DataFrame from Athena query using Boto3? & Implementation & 0.84 & 0.86 & 0.81 \\
%    52309496 & What is the difference between Kubernetes Objects and Resources? & Contrastive & 0.71 & 0.70 & 0.63 \\
%    52759455 & How to configure parallel agent jobs in VSTS? & Implementation & 0.73 & 0.72 & 0.77 \\
%    53271935 & What is difference between Deployment Group and Deployment Pool? & Contrastive & 0.87 & 0.84 & 0.86 \\
%    53796032 & Why does Lambda@Edge throw assumable role error? & Causal & 0.87 & 0.88 & 0.88 \\
%    54275063 & Connecting to an Azure Devops private NuGet in vs code & Contrastive & 0.95 & 0.89 & 0.94 \\
%    55757593 & What is the difference between Boot volume and Block volume? & Contrastive & 0.88 & 0.86 & 0.93 \\
%    55798565 & ERROR: build step 0 "gcr.io/cloud-builders/docker" failed: exit status 1 & Implementation & 0.53 & 0.49 & 0.71 \\
%    56386812 & How to fix Azure Pipeline Docker build failure? & Implementation & 0.78 & 0.84 & 0.87 \\
%    57151326 & Difference between the APIs `aws s3` and `aws s3api` when granting & Contrastive & 0.80 & 0.66 & 0.79 \\
%    57234110 & How to configure Azure DevOps Service Connections? & Implementation & 0.72 & 0.71 & 0.68 \\
%    57764237 & How to configure Kubernetes Ingress for external service? & Implementation & 0.72 & 0.73 & 0.84 \\
%    57929675 & How to fix Kubernetes Ingress 503 error? & Implementation & 0.74 & 0.79 & 0.82 \\
%    57945555 & How to execute commands in Kubernetes utils Pod? & Implementation & 0.81 & 0.79 & 0.86 \\
%    57975052 & How to configure cross-account ECR access? & Implementation & 0.66 & 0.65 & 0.80 \\
%    59132598 & How to fix Athena data type casting issue? & Implementation & 0.73 & 0.73 & 0.70 \\
%    59465327 & How to publish HTML reports in Azure Pipeline? & Implementation & 0.82 & 0.81 & 0.91 \\
%    59931761 & What is the Lambda unzipped deployment package size limit? & Implementation & 0.81 & 0.81 & 0.76 \\
%    60113774 & How to use multiple Terraform versions? & Implementation & 0.84 & 0.88 & 0.92 \\
%    60509976 & How to fix Azure build memory error? & Implementation & 0.55 & 0.54 & 0.60 \\
%    61616203 & How to fix Nginx Ingress webhook failure? & Implementation & 0.85 & 0.86 & 0.87 \\
%    61785338 & Why is the connection to S3 via a Gateway Endpoint timing out? & Causal & 0.84 & 0.83 & 0.81 \\
%    61971160 & Error creating IAM Role. MalformedPolicyDocument: Has prohibited field & Implementation & 0.79 & 0.77 & 0.77 \\
%    62256761 & How to create response card in AWS Lambda using C\#? & Implementation & 0.68 & 0.69 & 0.70 \\
%    64066461 & Kubernetes Failed to pull image no basic auth credentials & Other & 0.81 & 0.77 & 0.81 \\
%    64340585 & Best practice for id\_token vs.\ access\_token use in AWS Lambda & Contrastive & 0.70 & 0.71 & 0.71 \\
%    64512182 & Cosmos DB: The request failed because the client was unable to establish & Other & 0.83 & 0.83 & 0.88 \\
%    64555789 & What is the difference between VPC and cfnVPC in AWS CDK? & Contrastive & 0.64 & 0.66 & 0.75 \\
%    65035156 & How to configure Azure Pipeline PR trigger? & Implementation & 0.63 & 0.63 & 0.64 \\
%    65098456 & Error: Error: Failed to deploy web package to App Service. Bad Request & Implementation & 0.79 & 0.76 & 0.68 \\
%    65174549 & Azure DevOps Terraform Init - Remote State - Failed to get existing & Other & 0.30 & 0.30 & 0.35 \\
%    65180131 & How to handle Lambda singleton with token coordination? & Implementation & 0.83 & 0.84 & 0.83 \\
%    65993432 & Why is Azure DevOps Pipeline creating empty zip? & Implementation & 0.72 & 0.69 & 0.80 \\
%    66033629 & Azure Kubernetes Error when running "az aks get-credentials" command & Implementation & 0.78 & 0.82 & 0.87 \\
%    66165707 & NextJS: getStaticProps revalidate not working & Other & 0.68 & 0.68 & 0.85 \\
%    67766166 & Serverless Framework with localstack, where to view error output & Implementation & 0.74 & 0.69 & 0.74 \\
%    68054148 & Why does CloudFront sometimes serve gzip instead of br, when both are enabled? & Causal & 0.87 & 0.88 & 0.89 \\
%    68849201 & Disadvantage of using --allow-releaseinfo-change for apt-get update & Other & 0.80 & 0.64 & 0.85 \\
%    70566785 & How do I fix the AWS Wild Rydes sample error: InvalidParameterException: & Implementation & 0.78 & 0.79 & 0.79 \\
%    70873798 & FastApi on uvicorn works good but fails to work with Docker and docker-compose & Other & 0.84 & 0.73 & 0.72 \\
%    71654588 & How to fix Heroku memory quota error with Selenium? & Implementation & 0.73 & 0.67 & 0.76 \\
%    72553425 & Why firebase emulator function says request body is missing data? & Causal & 0.74 & 0.77 & 0.76 \\
%    72874999 & How to detect Kubernetes CNI type? & Implementation & 0.81 & 0.70 & 0.80 \\
%    73260296 & Why does traffic not route between AWS VPC subnets? & Causal & 0.78 & 0.82 & 0.80 \\
%    74446497 & ssh connection failed for ec2 as there is no space left on ec2 instance & Other & 0.69 & 0.60 & 0.53 \\
%    \hline
%    \multicolumn{3}{r}{\textbf{\hl{Mean (SD)---earlier comparison run} %MDPI: RESPONSE: Please leave as is.
%}} & \textbf{\hl{0.75 (0.13)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.73 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.76 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} \\
   
   
%    708910 & How does Google App Engine compare to web hosting plans? & Contrastive & 0.76 & 0.72 & 0.85 \\
%    1872120 & Why can't I see any data in the Google App Engine *Development* Console? & Causal & 0.71 & 0.70 & 0.66 \\
%    2207227 & What happens to EC2 instance when it is stopped with instance store? & Implementation & 0.78 & 0.83 & 0.82 \\
%    2623773 & How to upload large files to Azure Blob Storage? & Implementation & 0.80 & 0.74 & 0.75 \\
%    2652820 & Error on windows using session from appengine-utilities & Implementation & 0.82 & 0.81 & 0.84 \\
%   2656068 & How do I send email from a web2py application on Google App Engine? & Implementation & 0.91 & 0.83 & 0.86 \\
%    3098662 & what is the difference between Space-based computing and Cloud computing & Contrastive & 0.81 & 0.82 & 0.90 \\
%    3408945 & What is the fastest way to check object existence in Google App Engine? & Implementation & 0.84 & 0.51 & 0.52 \\
%    4073627 & How to use mix-in properties in Google App Engine model? & Implementation & 0.89 & 0.55 & 0.58 \\
%    4913891 & How to run GAE cron jobs on a specific version? & Implementation & 0.79 & 0.71 & 0.87 \\
%    4942622 & How to rollback database migration in Heroku? & Implementation & 0.87 & 0.84 & 0.90 \\
%    5223763 & How to use Ruby FTP without saving file to disk? & Implementation & 0.87 & 0.53 & 0.43 \\
%    6222673 & Rails 3.1 Deployment to Heroku Error & Implementation & 0.68 & 0.56 & 0.68 \\
%    8740494 & Amazon S3, why is logging turn off by default and does turning it on have & Causal & 0.67 & 0.68 & 0.69 \\
%    9256008 & What files should be included in Azure package with msbuild? & Implementation & 0.76 & 0.55 & 0.61 \\
%    9648559 & How to push to Heroku when behind a proxy? & Implementation & 0.82 & 0.78 & 0.82 \\
%    9831129 & failed to push some refs to git@heroku.com:myapp.git & Other & 0.60 & 0.59 & 0.62 \\
%    11343462 & How does App Engine send mail function work? & Implementation & 0.81 & 0.82 & 0.88 \\
%    12486730 & What is the best practice for Azure Web Roles scaling threshold? & Best Practice & 0.76 & 0.80 & 0.83 \\
%    15833039 & How to redirect www to non-www in AWS Route 53? & Implementation & 0.79 & 0.80 & 0.80 \\
%    18267749 & Why does Python App Engine Translate API error occur? & Causal & 0.73 & 0.72 & 0.67 \\
%    18510846 & JMeter latency vs load time(sample time) & Contrastive & 0.60 & 0.69 & 0.73 \\
%    18559099 & How to fix RDS SQL super privilege error? & Implementation & 0.68 & 0.69 & 0.66 \\
%    18952620 & css not working in email sending using AWS & Other & 0.26 & 0.35 & 0.43 \\
%    19165030 & ActionView::Template::Error (PG::UndefinedColumn: ERROR: column & Implementation & 0.57 & 0.77 & 0.71 \\
%    21069105 & Loading data (incrementally) into Amazon Redshift, S3 vs DynamoDB vs Insert & Contrastive & 0.81 & 0.87 & 0.86 \\
%    22220837 & How to partition by date in Hive? & Implementation & 0.88 & 0.87 & 0.89 \\
%    23665676 & JW Player - Error loading player: HTML5 player not found in Rails 3.2 app & Implementation & 0.72 & 0.76 & 0.80 \\
%    24192210 & Docker build npm error logs & Implementation & 0.72 & 0.72 & 0.75 \\
%    26827754 & Error while running Update-Database telling me that cannot create more & Implementation & 0.54 & 0.41 & 0.37 \\
%    27230199 & How to generate SAS url for Azure File Storage? & Implementation & 0.87 & 0.77 & 0.84 \\
%    28625899 & Any AWS Powershell command fails with message "Invalid URI: The hostname & Other & 0.84 & 0.81 & 0.88 \\
%    29092758 & How to calculate median in BigQuery? & Implementation & 0.61 & 0.55 & 0.45 \\
%    29239053 & Azure Storage Emulator fails after adding VS 2015 SDK & Contrastive & 0.85 & 0.86 & 0.84 \\
%    30543381 & Resque tasks always fail - uninitialized job constants? & Other & 0.86 & 0.89 & 0.95 \\
    31149501 & How to resolve Docker container names? & Implementation & 0.67 & 0.65 & 0.75 \\
    32618437 & Why can't I access my Kubernetes service via its IP? & Causal & 0.81 & 0.84 & 0.85 \\
    33377022 & How to copy files from Docker container to host? & Implementation & 0.83 & 0.81 & 0.94 \\
    34411293 & Does CloudFront configuration survive reboot? & Implementation & 0.64 & 0.50 & 0.46 \\
    34740610 & How to fix GAE deployment PageSpeed warning? & Implementation & 0.88 & 0.87 & 0.88 \\
    34800976 & How to trigger Lambda from CloudWatch alarm? & Implementation & 0.82 & 0.81 & 0.83 \\
    35368337 & How to setup IAM policy for AWS Lambda in VPC to resolve error "You are & Implementation & 0.89 & 0.87 & 0.91 \\
    35878619 & What is the maximum frequency for Lambda tasks? & Implementation & 0.84 & 0.88 & 0.88 \\
    36861007 & Strongloop/Loopback - Get Started - Service manager failed to start & Other & 0.37 & 0.32 & 0.40 \\
    38416528 & AWS CLI 'cp' fails with "The specified bucket does not exist" & Other & 0.88 & 0.79 & 0.85 \\
    38478451 & Gae/cloudsql error: Access denied for user 'root'@'cloudsqlproxy & Implementation & 0.56 & 0.51 & 0.52 \\
    39138488 & How to start Docker container conditionally? & Implementation & 0.78 & 0.78 & 0.78 \\
    39772259 & How to configure API Gateway Lambda proxy integration? & Implementation & 0.81 & 0.82 & 0.82 \\
    40335179 & How to resize Kubernetes persistent volume? & Implementation & 0.89 & 0.82 & 0.85 \\
    40704760 & How to fix UserSecretsId attribute error? & Implementation & 0.81 & 0.78 & 0.88 \\
    40863417 & How to resolve CORS issue in Vue.js application? & Implementation & 0.63 & 0.52 & 0.58 \\
    41404527 & How to download S3 image to ImageView? & Implementation & 0.70 & 0.87 & 0.88 \\
    41552392 & AWS Lambda code is throwing "An error occurred (UnauthorizedOperation) & Implementation & 0.71 & 0.68 & 0.80 \\
    42523583 & GZIP not working in Amazon Cloudfront & Other & 0.63 & 0.56 & 0.60 \\
    42545431 & What is the difference between Docker-Compose and Docker-Swarm? & Contrastive & 0.86 & 0.88 & 0.91 \\
    43044883 & Why Kubernetes Pod gets into Terminated state giving Completed reason and & Causal & 0.76 & 0.63 & 0.65 \\
    43517984 & docker-compose stop not working after docker-compose -p <name> up & Other & 0.83 & 0.83 & 0.81 \\
    43637519 & Why is Cloud Functions for Firebase taking 25 s? & Causal & 0.29 & 0.22 & 0.39 \\
     \bottomrule
\end{tabularx}
\end{table}


\begin{table}[H]\ContinuedFloat  
\small
    \caption{\emph{Cont.}}\label{tab:nerdengine-evaluation-results}
\setlength{\tabcolsep}{2.5pt}
\renewcommand{\arraystretch}{1.25}
\begin{tabularx}{\textwidth}{>{\centering\arraybackslash}m{1.2cm}>{\RaggedRight\arraybackslash}X>{\RaggedRight\arraybackslash}m{3.1cm}>{\centering\arraybackslash}m{0.68cm}>{\centering\arraybackslash}p{0.68cm}>{\centering\arraybackslash}m{0.62cm}}
    
    \toprule
    \textbf{ID} & \textbf{Question} & \textbf{Category} & \textbf{CN} & \textbf{ODR} & \textbf{AG} \\
    \midrule
    44833242 & Docker Compose vs. Multi-Stage Build & Contrastive & 0.91 & 0.91 & 0.94 \\
    45295838 & Amazon Redshift error: Mandatory url not present in manifest file & Implementation & 0.62 & 0.62 & 0.73 \\
    45734959 & AWS S3 error when there are Chinese characters in the userMetaData of & Implementation & 0.77 & 0.79 & 0.76 \\
    45776441 & Error:scalac: Class com.amazonaws.services.s3.event.S3 EventNotification & Implementation & 0.76 & 0.76 & 0.80 \\
    46757614 & How to update array field in Firestore? & Implementation & 0.89 & 0.88 & 0.92 \\
    46797662 & How to retrieve Cloud Function version information? & Implementation & 0.73 & 0.79 & 0.76 \\
    47051610 & Kubernetes Minikube on Windows Pod failed to pull image & Other & 0.60 & 0.67 & 0.69 \\
%    47710767 & How does depends\_on work in Docker-compose v3? & Implementation & 0.84 & 0.84 & 0.80 \\
%    47939068 & AccessDenied Invalid according to Policy: Policy Condition failed: & Other & 0.65 & 0.63 & 0.69 \\
%    48268584 & AWS-SDK Load Error in AWS Lambda function using NodeJS & Implementation & 0.84 & 0.84 & 0.89 \\
%    48624576 & An error occurred (AccessDenied) when calling the DeleteBucket operation: & Implementation & 0.93 & 0.90 & 0.92 \\
%    48862162 & VSTS and Git: Why does squashing my DEV branch while merging to master & Contrastive & 0.83 & 0.79 & 0.85 \\
%    48927312 & MountVolume.SetUp failed for volume "mongo" : hostPath type check failed: & Implementation & 0.54 & 0.59 & 0.61 \\
%    48951840 & How to create Star Rating report in BigQuery? & Implementation & 0.80 & 0.71 & 0.86 \\
%    50140885 & What is the difference between SAM and CloudFormation template? & Contrastive & 0.87 & 0.86 & 0.94 \\
%    50937959 & Zappa deployment error : GET request yields 502 response code & Implementation & 0.74 & 0.56 & 0.66 \\
%    50981864 & How to route traffic from NLB to ALB? & Implementation & 0.82 & 0.80 & 0.82 \\
%    52026405 & How to create DataFrame from Athena query using Boto3? & Implementation & 0.84 & 0.86 & 0.81 \\
%    52309496 & What is the difference between Kubernetes Objects and Resources? & Contrastive & 0.71 & 0.70 & 0.63 \\
%    52759455 & How to configure parallel agent jobs in VSTS? & Implementation & 0.73 & 0.72 & 0.77 \\
%    53271935 & What is difference between Deployment Group and Deployment Pool? & Contrastive & 0.87 & 0.84 & 0.86 \\
%    53796032 & Why does Lambda@Edge throw assumable role error? & Causal & 0.87 & 0.88 & 0.88 \\
%    54275063 & Connecting to an Azure Devops private NuGet in vs code & Contrastive & 0.95 & 0.89 & 0.94 \\
%    55757593 & What is the difference between Boot volume and Block volume? & Contrastive & 0.88 & 0.86 & 0.93 \\
%    55798565 & ERROR: build step 0 "gcr.io/cloud-builders/docker" failed: exit status 1 & Implementation & 0.53 & 0.49 & 0.71 \\
%    56386812 & How to fix Azure Pipeline Docker build failure? & Implementation & 0.78 & 0.84 & 0.87 \\
%    57151326 & Difference between the APIs `aws s3` and `aws s3api` when granting & Contrastive & 0.80 & 0.66 & 0.79 \\
%    57234110 & How to configure Azure DevOps Service Connections? & Implementation & 0.72 & 0.71 & 0.68 \\
%    57764237 & How to configure Kubernetes Ingress for external service? & Implementation & 0.72 & 0.73 & 0.84 \\
%    57929675 & How to fix Kubernetes Ingress 503 error? & Implementation & 0.74 & 0.79 & 0.82 \\
%    57945555 & How to execute commands in Kubernetes utils Pod? & Implementation & 0.81 & 0.79 & 0.86 \\
%    57975052 & How to configure cross-account ECR access? & Implementation & 0.66 & 0.65 & 0.80 \\
%    59132598 & How to fix Athena data type casting issue? & Implementation & 0.73 & 0.73 & 0.70 \\
%    59465327 & How to publish HTML reports in Azure Pipeline? & Implementation & 0.82 & 0.81 & 0.91 \\
%    59931761 & What is the Lambda unzipped deployment package size limit? & Implementation & 0.81 & 0.81 & 0.76 \\
%    60113774 & How to use multiple Terraform versions? & Implementation & 0.84 & 0.88 & 0.92 \\
%    60509976 & How to fix Azure build memory error? & Implementation & 0.55 & 0.54 & 0.60 \\
%    61616203 & How to fix Nginx Ingress webhook failure? & Implementation & 0.85 & 0.86 & 0.87 \\
%    61785338 & Why is the connection to S3 via a Gateway Endpoint timing out? & Causal & 0.84 & 0.83 & 0.81 \\
%    61971160 & Error creating IAM Role. MalformedPolicyDocument: Has prohibited field & Implementation & 0.79 & 0.77 & 0.77 \\
%    62256761 & How to create response card in AWS Lambda using C\#? & Implementation & 0.68 & 0.69 & 0.70 \\
%    64066461 & Kubernetes Failed to pull image no basic auth credentials & Other & 0.81 & 0.77 & 0.81 \\
%    64340585 & Best practice for id\_token vs.\ access\_token use in AWS Lambda & Contrastive & 0.70 & 0.71 & 0.71 \\
%    64512182 & Cosmos DB: The request failed because the client was unable to establish & Other & 0.83 & 0.83 & 0.88 \\
%    64555789 & What is the difference between VPC and cfnVPC in AWS CDK? & Contrastive & 0.64 & 0.66 & 0.75 \\
%    65035156 & How to configure Azure Pipeline PR trigger? & Implementation & 0.63 & 0.63 & 0.64 \\
%    65098456 & Error: Error: Failed to deploy web package to App Service. Bad Request & Implementation & 0.79 & 0.76 & 0.68 \\
%    65174549 & Azure DevOps Terraform Init - Remote State - Failed to get existing & Other & 0.30 & 0.30 & 0.35 \\
%    65180131 & How to handle Lambda singleton with token coordination? & Implementation & 0.83 & 0.84 & 0.83 \\
%    65993432 & Why is Azure DevOps Pipeline creating empty zip? & Implementation & 0.72 & 0.69 & 0.80 \\
%    66033629 & Azure Kubernetes Error when running "az aks get-credentials" command & Implementation & 0.78 & 0.82 & 0.87 \\
%    66165707 & NextJS: getStaticProps revalidate not working & Other & 0.68 & 0.68 & 0.85 \\
%    67766166 & Serverless Framework with localstack, where to view error output & Implementation & 0.74 & 0.69 & 0.74 \\
%    68054148 & Why does CloudFront sometimes serve gzip instead of br, when both are enabled? & Causal & 0.87 & 0.88 & 0.89 \\
%    68849201 & Disadvantage of using --allow-releaseinfo-change for apt-get update & Other & 0.80 & 0.64 & 0.85 \\
%    70566785 & How do I fix the AWS Wild Rydes sample error: InvalidParameterException: & Implementation & 0.78 & 0.79 & 0.79 \\
%    70873798 & FastApi on uvicorn works good but fails to work with Docker and docker-compose & Other & 0.84 & 0.73 & 0.72 \\
%    71654588 & How to fix Heroku memory quota error with Selenium? & Implementation & 0.73 & 0.67 & 0.76 \\
%    72553425 & Why firebase emulator function says request body is missing data? & Causal & 0.74 & 0.77 & 0.76 \\
%    72874999 & How to detect Kubernetes CNI type? & Implementation & 0.81 & 0.70 & 0.80 \\
%    73260296 & Why does traffic not route between AWS VPC subnets? & Causal & 0.78 & 0.82 & 0.80 \\
%    74446497 & ssh connection failed for ec2 as there is no space left on ec2 instance & Other & 0.69 & 0.60 & 0.53 \\
%    \hline
%    \multicolumn{3}{r}{\textbf{\hl{Mean (SD)---earlier comparison run} %MDPI: RESPONSE: Please leave as is.
%}} & \textbf{\hl{0.75 (0.13)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.73 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.76 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} \\
   
   
%    708910 & How does Google App Engine compare to web hosting plans? & Contrastive & 0.76 & 0.72 & 0.85 \\
%    1872120 & Why can't I see any data in the Google App Engine *Development* Console? & Causal & 0.71 & 0.70 & 0.66 \\
%    2207227 & What happens to EC2 instance when it is stopped with instance store? & Implementation & 0.78 & 0.83 & 0.82 \\
%    2623773 & How to upload large files to Azure Blob Storage? & Implementation & 0.80 & 0.74 & 0.75 \\
%    2652820 & Error on windows using session from appengine-utilities & Implementation & 0.82 & 0.81 & 0.84 \\
%   2656068 & How do I send email from a web2py application on Google App Engine? & Implementation & 0.91 & 0.83 & 0.86 \\
%    3098662 & what is the difference between Space-based computing and Cloud computing & Contrastive & 0.81 & 0.82 & 0.90 \\
%    3408945 & What is the fastest way to check object existence in Google App Engine? & Implementation & 0.84 & 0.51 & 0.52 \\
%    4073627 & How to use mix-in properties in Google App Engine model? & Implementation & 0.89 & 0.55 & 0.58 \\
%    4913891 & How to run GAE cron jobs on a specific version? & Implementation & 0.79 & 0.71 & 0.87 \\
%    4942622 & How to rollback database migration in Heroku? & Implementation & 0.87 & 0.84 & 0.90 \\
%    5223763 & How to use Ruby FTP without saving file to disk? & Implementation & 0.87 & 0.53 & 0.43 \\
%    6222673 & Rails 3.1 Deployment to Heroku Error & Implementation & 0.68 & 0.56 & 0.68 \\
%    8740494 & Amazon S3, why is logging turn off by default and does turning it on have & Causal & 0.67 & 0.68 & 0.69 \\
%    9256008 & What files should be included in Azure package with msbuild? & Implementation & 0.76 & 0.55 & 0.61 \\
%    9648559 & How to push to Heroku when behind a proxy? & Implementation & 0.82 & 0.78 & 0.82 \\
%    9831129 & failed to push some refs to git@heroku.com:myapp.git & Other & 0.60 & 0.59 & 0.62 \\
%    11343462 & How does App Engine send mail function work? & Implementation & 0.81 & 0.82 & 0.88 \\
%    12486730 & What is the best practice for Azure Web Roles scaling threshold? & Best Practice & 0.76 & 0.80 & 0.83 \\
%    15833039 & How to redirect www to non-www in AWS Route 53? & Implementation & 0.79 & 0.80 & 0.80 \\
%    18267749 & Why does Python App Engine Translate API error occur? & Causal & 0.73 & 0.72 & 0.67 \\
%    18510846 & JMeter latency vs load time(sample time) & Contrastive & 0.60 & 0.69 & 0.73 \\
%    18559099 & How to fix RDS SQL super privilege error? & Implementation & 0.68 & 0.69 & 0.66 \\
%    18952620 & css not working in email sending using AWS & Other & 0.26 & 0.35 & 0.43 \\
%    19165030 & ActionView::Template::Error (PG::UndefinedColumn: ERROR: column & Implementation & 0.57 & 0.77 & 0.71 \\
%    21069105 & Loading data (incrementally) into Amazon Redshift, S3 vs DynamoDB vs Insert & Contrastive & 0.81 & 0.87 & 0.86 \\
%    22220837 & How to partition by date in Hive? & Implementation & 0.88 & 0.87 & 0.89 \\
%    23665676 & JW Player - Error loading player: HTML5 player not found in Rails 3.2 app & Implementation & 0.72 & 0.76 & 0.80 \\
%    24192210 & Docker build npm error logs & Implementation & 0.72 & 0.72 & 0.75 \\
%    26827754 & Error while running Update-Database telling me that cannot create more & Implementation & 0.54 & 0.41 & 0.37 \\
%    27230199 & How to generate SAS url for Azure File Storage? & Implementation & 0.87 & 0.77 & 0.84 \\
%    28625899 & Any AWS Powershell command fails with message "Invalid URI: The hostname & Other & 0.84 & 0.81 & 0.88 \\
%    29092758 & How to calculate median in BigQuery? & Implementation & 0.61 & 0.55 & 0.45 \\
%    29239053 & Azure Storage Emulator fails after adding VS 2015 SDK & Contrastive & 0.85 & 0.86 & 0.84 \\
%    30543381 & Resque tasks always fail - uninitialized job constants? & Other & 0.86 & 0.89 & 0.95 \\
%    31149501 & How to resolve Docker container names? & Implementation & 0.67 & 0.65 & 0.75 \\
%    32618437 & Why can't I access my Kubernetes service via its IP? & Causal & 0.81 & 0.84 & 0.85 \\
%    33377022 & How to copy files from Docker container to host? & Implementation & 0.83 & 0.81 & 0.94 \\
%    34411293 & Does CloudFront configuration survive reboot? & Implementation & 0.64 & 0.50 & 0.46 \\
%    34740610 & How to fix GAE deployment PageSpeed warning? & Implementation & 0.88 & 0.87 & 0.88 \\
%    34800976 & How to trigger Lambda from CloudWatch alarm? & Implementation & 0.82 & 0.81 & 0.83 \\
%    35368337 & How to setup IAM policy for AWS Lambda in VPC to resolve error "You are & Implementation & 0.89 & 0.87 & 0.91 \\
%    35878619 & What is the maximum frequency for Lambda tasks? & Implementation & 0.84 & 0.88 & 0.88 \\
%    36861007 & Strongloop/Loopback - Get Started - Service manager failed to start & Other & 0.37 & 0.32 & 0.40 \\
%    38416528 & AWS CLI 'cp' fails with "The specified bucket does not exist" & Other & 0.88 & 0.79 & 0.85 \\
%    38478451 & Gae/cloudsql error: Access denied for user 'root'@'cloudsqlproxy & Implementation & 0.56 & 0.51 & 0.52 \\
%    39138488 & How to start Docker container conditionally? & Implementation & 0.78 & 0.78 & 0.78 \\
%    39772259 & How to configure API Gateway Lambda proxy integration? & Implementation & 0.81 & 0.82 & 0.82 \\
%    40335179 & How to resize Kubernetes persistent volume? & Implementation & 0.89 & 0.82 & 0.85 \\
%    40704760 & How to fix UserSecretsId attribute error? & Implementation & 0.81 & 0.78 & 0.88 \\
%    40863417 & How to resolve CORS issue in Vue.js application? & Implementation & 0.63 & 0.52 & 0.58 \\
%    41404527 & How to download S3 image to ImageView? & Implementation & 0.70 & 0.87 & 0.88 \\
%    41552392 & AWS Lambda code is throwing "An error occurred (UnauthorizedOperation) & Implementation & 0.71 & 0.68 & 0.80 \\
%    42523583 & GZIP not working in Amazon Cloudfront & Other & 0.63 & 0.56 & 0.60 \\
%    42545431 & What is the difference between Docker-Compose and Docker-Swarm? & Contrastive & 0.86 & 0.88 & 0.91 \\
%    43044883 & Why Kubernetes Pod gets into Terminated state giving Completed reason and & Causal & 0.76 & 0.63 & 0.65 \\
%    43517984 & docker-compose stop not working after docker-compose -p <name> up & Other & 0.83 & 0.83 & 0.81 \\
%    43637519 & Why is Cloud Functions for Firebase taking 25 s? & Causal & 0.29 & 0.22 & 0.39 \\
%    44833242 & Docker Compose vs Multi-Stage Build & Contrastive & 0.91 & 0.91 & 0.94 \\
%    45295838 & Amazon Redshift error: Mandatory url not present in manifest file & Implementation & 0.62 & 0.62 & 0.73 \\
%    45734959 & AWS S3 error when there are Chinese characters in the userMetaData of & Implementation & 0.77 & 0.79 & 0.76 \\
%    45776441 & Error:scalac: Class com.amazonaws.services.s3.event.S3 EventNotification & Implementation & 0.76 & 0.76 & 0.80 \\
%    46757614 & How to update array field in Firestore? & Implementation & 0.89 & 0.88 & 0.92 \\
%    46797662 & How to retrieve Cloud Function version information? & Implementation & 0.73 & 0.79 & 0.76 \\
%    47051610 & Kubernetes Minikube on Windows Pod failed to pull image & Other & 0.60 & 0.67 & 0.69 \\
    47710767 & How does depends\_on work in Docker-compose v3? & Implementation & 0.84 & 0.84 & 0.80 \\
    47939068 & AccessDenied Invalid according to Policy: Policy Condition failed: & Other & 0.65 & 0.63 & 0.69 \\
    48268584 & AWS-SDK Load Error in AWS Lambda function using NodeJS & Implementation & 0.84 & 0.84 & 0.89 \\
    48624576 & An error occurred (AccessDenied) when calling the DeleteBucket operation: & Implementation & 0.93 & 0.90 & 0.92 \\
    48862162 & VSTS and Git: Why does squashing my DEV branch while merging to master & Contrastive & 0.83 & 0.79 & 0.85 \\
    48927312 & MountVolume.SetUp failed for volume "mongo" : hostPath type check failed: & Implementation & 0.54 & 0.59 & 0.61 \\
    48951840 & How to create Star Rating report in BigQuery? & Implementation & 0.80 & 0.71 & 0.86 \\
    50140885 & What is the difference between SAM and CloudFormation template? & Contrastive & 0.87 & 0.86 & 0.94 \\
    50937959 & Zappa deployment error : GET request yields 502 response code & Implementation & 0.74 & 0.56 & 0.66 \\
    50981864 & How to route traffic from NLB to ALB? & Implementation & 0.82 & 0.80 & 0.82 \\
    52026405 & How to create DataFrame from Athena query using Boto3? & Implementation & 0.84 & 0.86 & 0.81 \\
    52309496 & What is the difference between Kubernetes Objects and Resources? & Contrastive & 0.71 & 0.70 & 0.63 \\
    52759455 & How to configure parallel agent jobs in VSTS? & Implementation & 0.73 & 0.72 & 0.77 \\
    53271935 & What is difference between Deployment Group and Deployment Pool? & Contrastive & 0.87 & 0.84 & 0.86 \\
    53796032 & Why does Lambda@Edge throw assumable role error? & Causal & 0.87 & 0.88 & 0.88 \\
    54275063 & Connecting to an Azure Devops private NuGet in vs code & Contrastive & 0.95 & 0.89 & 0.94 \\
    55757593 & What is the difference between Boot volume and Block volume? & Contrastive & 0.88 & 0.86 & 0.93 \\
    55798565 & ERROR: build step 0 "gcr.io/cloud-builders/docker" failed: exit status 1 & Implementation & 0.53 & 0.49 & 0.71 \\
    56386812 & How to fix Azure Pipeline Docker build failure? & Implementation & 0.78 & 0.84 & 0.87 \\
    57151326 & Difference between the APIs `aws s3` and `aws s3api` when granting & Contrastive & 0.80 & 0.66 & 0.79 \\
    57234110 & How to configure Azure DevOps Service Connections? & Implementation & 0.72 & 0.71 & 0.68 \\
    57764237 & How to configure Kubernetes Ingress for external service? & Implementation & 0.72 & 0.73 & 0.84 \\
     \bottomrule
\end{tabularx}
\end{table}


\begin{table}[H]\ContinuedFloat  
\small
    \caption{\emph{Cont.}}\label{tab:nerdengine-evaluation-results}
\setlength{\tabcolsep}{2.5pt}
\renewcommand{\arraystretch}{1.25}
\begin{tabularx}{\textwidth}{>{\centering\arraybackslash}m{1.2cm}>{\RaggedRight\arraybackslash}X>{\RaggedRight\arraybackslash}m{3.1cm}>{\centering\arraybackslash}m{0.68cm}>{\centering\arraybackslash}p{0.68cm}>{\centering\arraybackslash}m{0.62cm}}
    
    \toprule
    \textbf{ID} & \textbf{Question} & \textbf{Category} & \textbf{CN} & \textbf{ODR} & \textbf{AG} \\
    \midrule
    57929675 & How to fix Kubernetes Ingress 503 error? & Implementation & 0.74 & 0.79 & 0.82 \\
    57945555 & How to execute commands in Kubernetes utils Pod? & Implementation & 0.81 & 0.79 & 0.86 \\
    57975052 & How to configure cross-account ECR access? & Implementation & 0.66 & 0.65 & 0.80 \\
    59132598 & How to fix Athena data type casting issue? & Implementation & 0.73 & 0.73 & 0.70 \\
    59465327 & How to publish HTML reports in Azure Pipeline? & Implementation & 0.82 & 0.81 & 0.91 \\
    59931761 & What is the Lambda unzipped deployment package size limit? & Implementation & 0.81 & 0.81 & 0.76 \\
    60113774 & How to use multiple Terraform versions? & Implementation & 0.84 & 0.88 & 0.92 \\
    60509976 & How to fix Azure build memory error? & Implementation & 0.55 & 0.54 & 0.60 \\
    61616203 & How to fix Nginx Ingress webhook failure? & Implementation & 0.85 & 0.86 & 0.87 \\
%    61785338 & Why is the connection to S3 via a Gateway Endpoint timing out? & Causal & 0.84 & 0.83 & 0.81 \\
%    61971160 & Error creating IAM Role. MalformedPolicyDocument: Has prohibited field & Implementation & 0.79 & 0.77 & 0.77 \\
%    62256761 & How to create response card in AWS Lambda using C\#? & Implementation & 0.68 & 0.69 & 0.70 \\
%    64066461 & Kubernetes Failed to pull image no basic auth credentials & Other & 0.81 & 0.77 & 0.81 \\
%    64340585 & Best practice for id\_token vs.\ access\_token use in AWS Lambda & Contrastive & 0.70 & 0.71 & 0.71 \\
%    64512182 & Cosmos DB: The request failed because the client was unable to establish & Other & 0.83 & 0.83 & 0.88 \\
%    64555789 & What is the difference between VPC and cfnVPC in AWS CDK? & Contrastive & 0.64 & 0.66 & 0.75 \\
%    65035156 & How to configure Azure Pipeline PR trigger? & Implementation & 0.63 & 0.63 & 0.64 \\
%    65098456 & Error: Error: Failed to deploy web package to App Service. Bad Request & Implementation & 0.79 & 0.76 & 0.68 \\
%    65174549 & Azure DevOps Terraform Init - Remote State - Failed to get existing & Other & 0.30 & 0.30 & 0.35 \\
%    65180131 & How to handle Lambda singleton with token coordination? & Implementation & 0.83 & 0.84 & 0.83 \\
%    65993432 & Why is Azure DevOps Pipeline creating empty zip? & Implementation & 0.72 & 0.69 & 0.80 \\
%    66033629 & Azure Kubernetes Error when running "az aks get-credentials" command & Implementation & 0.78 & 0.82 & 0.87 \\
%    66165707 & NextJS: getStaticProps revalidate not working & Other & 0.68 & 0.68 & 0.85 \\
%    67766166 & Serverless Framework with localstack, where to view error output & Implementation & 0.74 & 0.69 & 0.74 \\
%    68054148 & Why does CloudFront sometimes serve gzip instead of br, when both are enabled? & Causal & 0.87 & 0.88 & 0.89 \\
%    68849201 & Disadvantage of using --allow-releaseinfo-change for apt-get update & Other & 0.80 & 0.64 & 0.85 \\
%    70566785 & How do I fix the AWS Wild Rydes sample error: InvalidParameterException: & Implementation & 0.78 & 0.79 & 0.79 \\
%    70873798 & FastApi on uvicorn works good but fails to work with Docker and docker-compose & Other & 0.84 & 0.73 & 0.72 \\
%    71654588 & How to fix Heroku memory quota error with Selenium? & Implementation & 0.73 & 0.67 & 0.76 \\
%    72553425 & Why firebase emulator function says request body is missing data? & Causal & 0.74 & 0.77 & 0.76 \\
%    72874999 & How to detect Kubernetes CNI type? & Implementation & 0.81 & 0.70 & 0.80 \\
%    73260296 & Why does traffic not route between AWS VPC subnets? & Causal & 0.78 & 0.82 & 0.80 \\
%    74446497 & ssh connection failed for ec2 as there is no space left on ec2 instance & Other & 0.69 & 0.60 & 0.53 \\
%    \hline
%    \multicolumn{3}{r}{\textbf{\hl{Mean (SD)---earlier comparison run} %MDPI: RESPONSE: Please leave as is.
%}} & \textbf{\hl{0.75 (0.13)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.73 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.76 (0.14)} %MDPI: RESPONSE: Please leave as is.
%} \\
    
   
%    708910 & How does Google App Engine compare to web hosting plans? & Contrastive & 0.76 & 0.72 & 0.85 \\
%    1872120 & Why can't I see any data in the Google App Engine *Development* Console? & Causal & 0.71 & 0.70 & 0.66 \\
%    2207227 & What happens to EC2 instance when it is stopped with instance store? & Implementation & 0.78 & 0.83 & 0.82 \\
%    2623773 & How to upload large files to Azure Blob Storage? & Implementation & 0.80 & 0.74 & 0.75 \\
%    2652820 & Error on windows using session from appengine-utilities & Implementation & 0.82 & 0.81 & 0.84 \\
%   2656068 & How do I send email from a web2py application on Google App Engine? & Implementation & 0.91 & 0.83 & 0.86 \\
%    3098662 & what is the difference between Space-based computing and Cloud computing & Contrastive & 0.81 & 0.82 & 0.90 \\
%    3408945 & What is the fastest way to check object existence in Google App Engine? & Implementation & 0.84 & 0.51 & 0.52 \\
%    4073627 & How to use mix-in properties in Google App Engine model? & Implementation & 0.89 & 0.55 & 0.58 \\
%    4913891 & How to run GAE cron jobs on a specific version? & Implementation & 0.79 & 0.71 & 0.87 \\
%    4942622 & How to rollback database migration in Heroku? & Implementation & 0.87 & 0.84 & 0.90 \\
%    5223763 & How to use Ruby FTP without saving file to disk? & Implementation & 0.87 & 0.53 & 0.43 \\
%    6222673 & Rails 3.1 Deployment to Heroku Error & Implementation & 0.68 & 0.56 & 0.68 \\
%    8740494 & Amazon S3, why is logging turn off by default and does turning it on have & Causal & 0.67 & 0.68 & 0.69 \\
%    9256008 & What files should be included in Azure package with msbuild? & Implementation & 0.76 & 0.55 & 0.61 \\
%    9648559 & How to push to Heroku when behind a proxy? & Implementation & 0.82 & 0.78 & 0.82 \\
%    9831129 & failed to push some refs to git@heroku.com:myapp.git & Other & 0.60 & 0.59 & 0.62 \\
%    11343462 & How does App Engine send mail function work? & Implementation & 0.81 & 0.82 & 0.88 \\
%    12486730 & What is the best practice for Azure Web Roles scaling threshold? & Best Practice & 0.76 & 0.80 & 0.83 \\
%    15833039 & How to redirect www to non-www in AWS Route 53? & Implementation & 0.79 & 0.80 & 0.80 \\
%    18267749 & Why does Python App Engine Translate API error occur? & Causal & 0.73 & 0.72 & 0.67 \\
%    18510846 & JMeter latency vs load time(sample time) & Contrastive & 0.60 & 0.69 & 0.73 \\
%    18559099 & How to fix RDS SQL super privilege error? & Implementation & 0.68 & 0.69 & 0.66 \\
%    18952620 & css not working in email sending using AWS & Other & 0.26 & 0.35 & 0.43 \\
%    19165030 & ActionView::Template::Error (PG::UndefinedColumn: ERROR: column & Implementation & 0.57 & 0.77 & 0.71 \\
%    21069105 & Loading data (incrementally) into Amazon Redshift, S3 vs DynamoDB vs Insert & Contrastive & 0.81 & 0.87 & 0.86 \\
%    22220837 & How to partition by date in Hive? & Implementation & 0.88 & 0.87 & 0.89 \\
%    23665676 & JW Player - Error loading player: HTML5 player not found in Rails 3.2 app & Implementation & 0.72 & 0.76 & 0.80 \\
%    24192210 & Docker build npm error logs & Implementation & 0.72 & 0.72 & 0.75 \\
%    26827754 & Error while running Update-Database telling me that cannot create more & Implementation & 0.54 & 0.41 & 0.37 \\
%    27230199 & How to generate SAS url for Azure File Storage? & Implementation & 0.87 & 0.77 & 0.84 \\
%    28625899 & Any AWS Powershell command fails with message "Invalid URI: The hostname & Other & 0.84 & 0.81 & 0.88 \\
%    29092758 & How to calculate median in BigQuery? & Implementation & 0.61 & 0.55 & 0.45 \\
%    29239053 & Azure Storage Emulator fails after adding VS 2015 SDK & Contrastive & 0.85 & 0.86 & 0.84 \\
%    30543381 & Resque tasks always fail - uninitialized job constants? & Other & 0.86 & 0.89 & 0.95 \\
%    31149501 & How to resolve Docker container names? & Implementation & 0.67 & 0.65 & 0.75 \\
%    32618437 & Why can't I access my Kubernetes service via its IP? & Causal & 0.81 & 0.84 & 0.85 \\
%    33377022 & How to copy files from Docker container to host? & Implementation & 0.83 & 0.81 & 0.94 \\
%    34411293 & Does CloudFront configuration survive reboot? & Implementation & 0.64 & 0.50 & 0.46 \\
%    34740610 & How to fix GAE deployment PageSpeed warning? & Implementation & 0.88 & 0.87 & 0.88 \\
%    34800976 & How to trigger Lambda from CloudWatch alarm? & Implementation & 0.82 & 0.81 & 0.83 \\
%    35368337 & How to setup IAM policy for AWS Lambda in VPC to resolve error "You are & Implementation & 0.89 & 0.87 & 0.91 \\
%    35878619 & What is the maximum frequency for Lambda tasks? & Implementation & 0.84 & 0.88 & 0.88 \\
%    36861007 & Strongloop/Loopback - Get Started - Service manager failed to start & Other & 0.37 & 0.32 & 0.40 \\
%    38416528 & AWS CLI 'cp' fails with "The specified bucket does not exist" & Other & 0.88 & 0.79 & 0.85 \\
%    38478451 & Gae/cloudsql error: Access denied for user 'root'@'cloudsqlproxy & Implementation & 0.56 & 0.51 & 0.52 \\
%    39138488 & How to start Docker container conditionally? & Implementation & 0.78 & 0.78 & 0.78 \\
%    39772259 & How to configure API Gateway Lambda proxy integration? & Implementation & 0.81 & 0.82 & 0.82 \\
%    40335179 & How to resize Kubernetes persistent volume? & Implementation & 0.89 & 0.82 & 0.85 \\
%    40704760 & How to fix UserSecretsId attribute error? & Implementation & 0.81 & 0.78 & 0.88 \\
%    40863417 & How to resolve CORS issue in Vue.js application? & Implementation & 0.63 & 0.52 & 0.58 \\
%    41404527 & How to download S3 image to ImageView? & Implementation & 0.70 & 0.87 & 0.88 \\
%    41552392 & AWS Lambda code is throwing "An error occurred (UnauthorizedOperation) & Implementation & 0.71 & 0.68 & 0.80 \\
%    42523583 & GZIP not working in Amazon Cloudfront & Other & 0.63 & 0.56 & 0.60 \\
%    42545431 & What is the difference between Docker-Compose and Docker-Swarm? & Contrastive & 0.86 & 0.88 & 0.91 \\
%    43044883 & Why Kubernetes Pod gets into Terminated state giving Completed reason and & Causal & 0.76 & 0.63 & 0.65 \\
%    43517984 & docker-compose stop not working after docker-compose -p <name> up & Other & 0.83 & 0.83 & 0.81 \\
%    43637519 & Why is Cloud Functions for Firebase taking 25 s? & Causal & 0.29 & 0.22 & 0.39 \\
%    44833242 & Docker Compose vs Multi-Stage Build & Contrastive & 0.91 & 0.91 & 0.94 \\
%    45295838 & Amazon Redshift error: Mandatory url not present in manifest file & Implementation & 0.62 & 0.62 & 0.73 \\
%    45734959 & AWS S3 error when there are Chinese characters in the userMetaData of & Implementation & 0.77 & 0.79 & 0.76 \\
%    45776441 & Error:scalac: Class com.amazonaws.services.s3.event.S3 EventNotification & Implementation & 0.76 & 0.76 & 0.80 \\
%    46757614 & How to update array field in Firestore? & Implementation & 0.89 & 0.88 & 0.92 \\
%    46797662 & How to retrieve Cloud Function version information? & Implementation & 0.73 & 0.79 & 0.76 \\
%    47051610 & Kubernetes Minikube on Windows Pod failed to pull image & Other & 0.60 & 0.67 & 0.69 \\
%    47710767 & How does depends\_on work in Docker-compose v3? & Implementation & 0.84 & 0.84 & 0.80 \\
%    47939068 & AccessDenied Invalid according to Policy: Policy Condition failed: & Other & 0.65 & 0.63 & 0.69 \\
%    48268584 & AWS-SDK Load Error in AWS Lambda function using NodeJS & Implementation & 0.84 & 0.84 & 0.89 \\
%    48624576 & An error occurred (AccessDenied) when calling the DeleteBucket operation: & Implementation & 0.93 & 0.90 & 0.92 \\
%    48862162 & VSTS and Git: Why does squashing my DEV branch while merging to master & Contrastive & 0.83 & 0.79 & 0.85 \\
%    48927312 & MountVolume.SetUp failed for volume "mongo" : hostPath type check failed: & Implementation & 0.54 & 0.59 & 0.61 \\
%    48951840 & How to create Star Rating report in BigQuery? & Implementation & 0.80 & 0.71 & 0.86 \\
%    50140885 & What is the difference between SAM and CloudFormation template? & Contrastive & 0.87 & 0.86 & 0.94 \\
%    50937959 & Zappa deployment error : GET request yields 502 response code & Implementation & 0.74 & 0.56 & 0.66 \\
%    50981864 & How to route traffic from NLB to ALB? & Implementation & 0.82 & 0.80 & 0.82 \\
%    52026405 & How to create DataFrame from Athena query using Boto3? & Implementation & 0.84 & 0.86 & 0.81 \\
%    52309496 & What is the difference between Kubernetes Objects and Resources? & Contrastive & 0.71 & 0.70 & 0.63 \\
%    52759455 & How to configure parallel agent jobs in VSTS? & Implementation & 0.73 & 0.72 & 0.77 \\
%    53271935 & What is difference between Deployment Group and Deployment Pool? & Contrastive & 0.87 & 0.84 & 0.86 \\
%    53796032 & Why does Lambda@Edge throw assumable role error? & Causal & 0.87 & 0.88 & 0.88 \\
%    54275063 & Connecting to an Azure Devops private NuGet in vs code & Contrastive & 0.95 & 0.89 & 0.94 \\
%    55757593 & What is the difference between Boot volume and Block volume? & Contrastive & 0.88 & 0.86 & 0.93 \\
%    55798565 & ERROR: build step 0 "gcr.io/cloud-builders/docker" failed: exit status 1 & Implementation & 0.53 & 0.49 & 0.71 \\
%    56386812 & How to fix Azure Pipeline Docker build failure? & Implementation & 0.78 & 0.84 & 0.87 \\
%    57151326 & Difference between the APIs `aws s3` and `aws s3api` when granting & Contrastive & 0.80 & 0.66 & 0.79 \\
%    57234110 & How to configure Azure DevOps Service Connections? & Implementation & 0.72 & 0.71 & 0.68 \\
%    57764237 & How to configure Kubernetes Ingress for external service? & Implementation & 0.72 & 0.73 & 0.84 \\
%    57929675 & How to fix Kubernetes Ingress 503 error? & Implementation & 0.74 & 0.79 & 0.82 \\
%    57945555 & How to execute commands in Kubernetes utils Pod? & Implementation & 0.81 & 0.79 & 0.86 \\
%    57975052 & How to configure cross-account ECR access? & Implementation & 0.66 & 0.65 & 0.80 \\
%    59132598 & How to fix Athena data type casting issue? & Implementation & 0.73 & 0.73 & 0.70 \\
%    59465327 & How to publish HTML reports in Azure Pipeline? & Implementation & 0.82 & 0.81 & 0.91 \\
%    59931761 & What is the Lambda unzipped deployment package size limit? & Implementation & 0.81 & 0.81 & 0.76 \\
%    60113774 & How to use multiple Terraform versions? & Implementation & 0.84 & 0.88 & 0.92 \\
%    60509976 & How to fix Azure build memory error? & Implementation & 0.55 & 0.54 & 0.60 \\
%    61616203 & How to fix Nginx Ingress webhook failure? & Implementation & 0.85 & 0.86 & 0.87 \\
    61785338 & Why is the connection to S3 via a Gateway Endpoint timing out? & Causal & 0.84 & 0.83 & 0.81 \\
    61971160 & Error creating IAM Role. MalformedPolicyDocument: Has prohibited field & Implementation & 0.79 & 0.77 & 0.77 \\
    62256761 & How to create response card in AWS Lambda using C\#? & Implementation & 0.68 & 0.69 & 0.70 \\
    64066461 & Kubernetes Failed to pull image no basic auth credentials & Other & 0.81 & 0.77 & 0.81 \\
    64340585 & Best practice for id\_token vs.\ access\_token use in AWS Lambda & Contrastive & 0.70 & 0.71 & 0.71 \\
    64512182 & Cosmos DB: The request failed because the client was unable to establish & Other & 0.83 & 0.83 & 0.88 \\
    64555789 & What is the difference between VPC and cfnVPC in AWS CDK? & Contrastive & 0.64 & 0.66 & 0.75 \\
    65035156 & How to configure Azure Pipeline PR trigger? & Implementation & 0.63 & 0.63 & 0.64 \\
    65098456 & Error: Error: Failed to deploy web package to App Service. Bad Request & Implementation & 0.79 & 0.76 & 0.68 \\
    65174549 & Azure DevOps Terraform Init - Remote State - Failed to get existing & Other & 0.30 & 0.30 & 0.35 \\
    65180131 & How to handle Lambda singleton with token coordination? & Implementation & 0.83 & 0.84 & 0.83 \\
    65993432 & Why is Azure DevOps Pipeline creating empty zip? & Implementation & 0.72 & 0.69 & 0.80 \\
    66033629 & Azure Kubernetes Error when running "az aks get-credentials" command & Implementation & 0.78 & 0.82 & 0.87 \\
    66165707 & NextJS: getStaticProps revalidate not working & Other & 0.68 & 0.68 & 0.85 \\
    67766166 & Serverless Framework with localstack, where to view error output & Implementation & 0.74 & 0.69 & 0.74 \\
    68054148 & Why does CloudFront sometimes serve gzip instead of br, when both are enabled? & Causal & 0.87 & 0.88 & 0.89 \\
    68849201 & Disadvantage of using --allow-releaseinfo-change for apt-get update & Other & 0.80 & 0.64 & 0.85 \\
    70566785 & How do I fix the AWS Wild Rydes sample error: InvalidParameterException: & Implementation & 0.78 & 0.79 & 0.79 \\
    70873798 & FastApi on uvicorn works good but fails to work with Docker and docker-compose & Other & 0.84 & 0.73 & 0.72 \\
     \bottomrule
\end{tabularx}
\end{table}


\begin{table}[H]\ContinuedFloat  
\small
    \caption{\emph{Cont.}}\label{tab:nerdengine-evaluation-results}
\setlength{\tabcolsep}{2.5pt}
\renewcommand{\arraystretch}{1.25}
\begin{tabularx}{\textwidth}{>{\centering\arraybackslash}m{1.2cm}>{\RaggedRight\arraybackslash}X>{\RaggedRight\arraybackslash}m{3.1cm}>{\centering\arraybackslash}m{0.68cm}>{\centering\arraybackslash}p{0.68cm}>{\centering\arraybackslash}m{0.62cm}}
    
    \toprule
    \textbf{ID} & \textbf{Question} & \textbf{Category} & \textbf{CN} & \textbf{ODR} & \textbf{AG} \\
    \midrule
    71654588 & How to fix Heroku memory quota error with Selenium? & Implementation & 0.73 & 0.67 & 0.76 \\
    72553425 & Why firebase emulator function says request body is missing data? & Causal & 0.74 & 0.77 & 0.76 \\
    72874999 & How to detect Kubernetes CNI type? & Implementation & 0.81 & 0.70 & 0.80 \\
    73260296 & Why does traffic not route between AWS VPC subnets? & Causal & 0.78 & 0.82 & 0.80 \\
    74446497 & ssh connection failed for ec2 as there is no space left on ec2 instance & Other & 0.69 & 0.60 & 0.53 \\
  \cline{1-6}
    \multicolumn{3}{r}{\textbf{\hl{Mean (SD)---earlier comparison run} %MDPI: RESPONSE: Please leave as is.
}} & \textbf{\hl{0.75 (0.13)} } & \vspace{-15pt}\textbf{\hl{0.73 (0.14)} } & \textbf{\hl{0.76 (0.14)} } \\
 \cline{1-6}
\end{tabularx}
\end{table}

\textit{\hl{Note:} %MDPI: RESPONSE: Please leave as is.
} Column headers CN, ODR, and~AG abbreviate CloudNerd, OpenDeepResearch, and~AutoGPT. Per-question CloudNerd means above are from an earlier CloudNerd configuration. The~evaluated CloudNerd STS mean is \textbf{\hl{0.695} %MDPI: RESPONSE: Please leave as is.
} (Table~\ref{tab:cloudnerd-aggregates-120}).

% % before 20 Feb 2026
% \begingroup
% \footnotesize
% \renewcommand{\arraystretch}{1.2} % Slightly more breathing room for long questions
% \begin{xltabular}{\textwidth}{|p{1.5cm}|X|X|p{1.8cm}|p{0.8cm}|p{0.8cm}|}
%     % --- Table Caption and Label ---
%     \caption{Semantic Similarity Scores: CloudNerd (CN) vs. DeepResearchEngine (DR) (60 questions, sorted by Question ID in ascending order)}
%     \label{tab:nerdengine-evaluation-results} \\
    
%     % --- Header for the First Page ---
%     \hline
%     \textbf{ID} & \textbf{Question} & \textbf{Category} & \textbf{CN} & \textbf{DR} \\
%     \hline
%     \endfirsthead

%     % --- Header for Subsequent Pages ---
%     \hline
%     \multicolumn{6}{|c|}{{Table \thetable\ (Continued)}} \\
%     \hline
%     \textbf{ID} & \textbf{Question} & \textbf{Category} & \textbf{CN} & \textbf{DR} \\
%     \hline
%     \endhead

%     % --- Footer for Pages (except the last) ---
%     \hline
%     \multicolumn{6}{|r|}{{Continued on next page...}} \\
%     \hline
%     \endfoot

%     % --- Final Footer ---
%     \hline
%     \endlastfoot

%     % --- Table Data ---
%     2207227 & EC2 instance store stop & What happens to EC2 instance when it is stopped with instance store? & Implementation & 0.78 & 0.85 \\
%     2623773 & Azure blob large file upload & How to upload large files to Azure Blob Storage? & Implementation & 0.80 & 0.74 \\
%     2656068 & Sending email from web2py on GAE & How do I send email from a web2py application on Google App Engine? & Implementation & 0.91 & 0.82 \\
%     3408945 & GAE fastest object existence check & What is the fastest way to check object existence in Google App Engine? & Implementation & 0.84 & 0.51 \\
%     4073627 & Mix-in properties in GAE model & How to use mix-in properties in Google App Engine model? & Implementation & 0.89 & 0.57 \\
%     4913891 & GAE cron jobs specific version & How to run GAE cron jobs on a specific version? & Implementation & 0.79 & 0.78 \\
%     4942622 & Heroku database migration rollback & How to rollback database migration in Heroku? & Implementation & 0.87 & 0.84 \\
%     5223763 & Ruby FTP without saving file & How to use Ruby FTP without saving file to disk? & Implementation & 0.87 & 0.64 \\
%     9256008 & Azure package msbuild files & What files should be included in Azure package with msbuild? & Implementation & 0.76 & 0.70 \\
%     9648559 & Heroku push behind proxy & How to push to Heroku when behind a proxy? & Implementation & 0.82 & 0.87 \\
%     11343462 & App Engine send mail function & How does App Engine send mail function work? & Implementation & 0.81 & 0.83 \\
%     12486730 & Azure Web Roles scaling threshold & What is the best practice for Azure Web Roles scaling threshold? & Best Practice & 0.76 & 0.80 \\
%     15833039 & AWS Route 53 www redirect & How to redirect www to non-www in AWS Route 53? & Implementation & 0.79 & 0.79 \\
%     18267749 & Python App Engine Translate API error & Why does Python App Engine Translate API error occur? & Causal & 0.73 & 0.73 \\
%     18559099 & RDS SQL super privilege error & How to fix RDS SQL super privilege error? & Implementation & 0.68 & 0.62 \\
%     22220837 & Hive partition by date & How to partition by date in Hive? & Implementation & 0.88 & 0.91 \\
%     27230199 & Azure File Storage SAS url & How to generate SAS url for Azure File Storage? & Implementation & 0.87 & 0.84 \\
%     29092758 & BigQuery median calculation & How to calculate median in BigQuery? & Implementation & 0.61 & 0.60 \\
%     31149501 & Docker container name resolution & How to resolve Docker container names? & Implementation & 0.67 & 0.63 \\
%     33377022 & Docker copy files to host & How to copy files from Docker container to host? & Implementation & 0.83 & 0.78 \\
%     34411293 & CloudFront reboot survival & Does CloudFront configuration survive reboot? & Implementation & 0.64 & 0.53 \\
%     34740610 & GAE deploy pagespeed warning & How to fix GAE deployment PageSpeed warning? & Implementation & 0.88 & 0.83 \\
%     34800976 & CloudWatch alarm to Lambda & How to trigger Lambda from CloudWatch alarm? & Implementation & 0.82 & 0.89 \\
%     35878619 & Lambda task frequency & What is the maximum frequency for Lambda tasks? & Implementation & 0.84 & 0.87 \\
%     39138488 & Docker container conditional start & How to start Docker container conditionally? & Implementation & 0.78 & 0.80 \\
%     39772259 & API Gateway Lambda proxy & How to configure API Gateway Lambda proxy integration? & Implementation & 0.81 & 0.82 \\
%     40335179 & Kubernetes PV resize & How to resize Kubernetes persistent volume? & Implementation & 0.89 & 0.77 \\
%     40704760 & UserSecretsId attribute error & How to fix UserSecretsId attribute error? & Implementation & 0.81 & 0.77 \\
%     40863417 & CORS issue Vue.js & How to resolve CORS issue in Vue.js application? & Implementation & 0.63 & 0.61 \\
%     41404527 & Download S3 image to ImageView & How to download S3 image to ImageView? & Implementation & 0.70 & 0.74 \\
%     42545431 & Docker-Compose vs Docker-Swarm & What is the difference between Docker-Compose and Docker-Swarm? & Contrastive & 0.86 & 0.86 \\
%     46757614 & Firestore array update & How to update array field in Firestore? & Implementation & 0.89 & 0.86 \\
%     46797662 & Cloud Function version retrieval & How to retrieve Cloud Function version information? & Implementation & 0.73 & 0.75 \\
%     47710767 & Docker-compose v3 depends\_on & How does depends\_on work in Docker-compose v3? & Implementation & 0.84 & 0.81 \\
%     48951840 & BigQuery Star Rating report & How to create Star Rating report in BigQuery? & Implementation & 0.80 & 0.83 \\
%     50140885 & SAM vs CloudFormation template & What is the difference between SAM and CloudFormation template? & Contrastive & 0.87 & 0.90 \\
%     50981864 & NLB to ALB traffic routing & How to route traffic from NLB to ALB? & Implementation & 0.82 & 0.83 \\
%     52026405 & Athena Boto3 DataFrame creation & How to create DataFrame from Athena query using Boto3? & Implementation & 0.84 & 0.86 \\
%     52309496 & Kubernetes Objects vs Resources & What is the difference between Kubernetes Objects and Resources? & Contrastive & 0.71 & 0.71 \\
%     52759455 & VSTS parallel agent jobs & How to configure parallel agent jobs in VSTS? & Implementation & 0.73 & 0.79 \\
%     53796032 & Lambda@Edge assumable role error & Why does Lambda@Edge throw assumable role error? & Causal & 0.87 & 0.84 \\
%     56386812 & Azure Pipeline Docker build failure & How to fix Azure Pipeline Docker build failure? & Implementation & 0.78 & 0.72 \\
%     57234110 & Azure DevOps Service Connections & How to configure Azure DevOps Service Connections? & Implementation & 0.72 & 0.67 \\
%     57764237 & Kubernetes Ingress external service & How to configure Kubernetes Ingress for external service? & Implementation & 0.72 & 0.68 \\
%     57929675 & Kubernetes Ingress 503 error & How to fix Kubernetes Ingress 503 error? & Implementation & 0.74 & 0.81 \\
%     57945555 & Kubernetes utils Pod execution & How to execute commands in Kubernetes utils Pod? & Implementation & 0.81 & 0.83 \\
%     57975052 & Cross-account ECR access & How to configure cross-account ECR access? & Implementation & 0.66 & 0.71 \\
%     59132598 & Athena casting issue & How to fix Athena data type casting issue? & Implementation & 0.73 & 0.56 \\
%     59465327 & Azure Pipeline HTML reports & How to publish HTML reports in Azure Pipeline? & Implementation & 0.82 & 0.83 \\
%     59931761 & Lambda unzipped size limit & What is the Lambda unzipped deployment package size limit? & Implementation & 0.81 & 0.82 \\
%     60113774 & Multiple Terraform versions & How to use multiple Terraform versions? & Implementation & 0.84 & 0.86 \\
%     60509976 & Azure build memory error & How to fix Azure build memory error? & Implementation & 0.55 & 0.51 \\
%     61616203 & Nginx Ingress webhook failure & How to fix Nginx Ingress webhook failure? & Implementation & 0.85 & 0.85 \\
%     62256761 & AWS Lambda response card C\# & How to create response card in AWS Lambda using C\#? & Implementation & 0.68 & 0.69 \\
%     65035156 & Azure Pipeline PR trigger & How to configure Azure Pipeline PR trigger? & Implementation & 0.63 & 0.64 \\
%     65180131 & Lambda singleton token coordination & How to handle Lambda singleton with token coordination? & Implementation & 0.83 & 0.81 \\
%     65993432 & Azure DevOps Pipeline empty zip & Why is Azure DevOps Pipeline creating empty zip? & Implementation & 0.72 & 0.74 \\
%     71654588 & Heroku memory quota Selenium & How to fix Heroku memory quota error with Selenium? & Implementation & 0.73 & 0.67 \\
%     72874999 & Kubernetes CNI type detection & How to detect Kubernetes CNI type? & Implementation & 0.81 & 0.75 \\
%     73260296 & AWS VPC subnet traffic & Why does traffic not route between AWS VPC subnets? & Causal & 0.78 & 0.79 \\
%     \hline
%     \multicolumn{4}{|r|}{\textbf{Mean (SD)}} & \textbf{0.78 (0.08)} & \textbf{0.71 (0.12)} \\
% \end{xltabular}
% \endgroup

% \begin{table}[p]  % Use [p] to put on separate page
% \centering
% \caption{Semantic Similarity Scores: \NerdEngine (CN) vs. DeepResearchEngine (DR) (60 Questions) \textcolor{red}{TBD: include full text of these questions and then eliminate appendix N. Make sure the references are to this appendix}}
% \label{tab:nerdengine-evaluation-results}
% \footnotesize  % Use footnotesize instead of small
% \renewcommand{\arraystretch}{1.0}  % Reduce from 1.2 to 1.0
% \begin{tabularx}{\textwidth}{|p{1.8cm}|X|p{2.2cm}|p{1.2cm}|p{1.5cm}|}
% \hline
% \textbf{ID} & \textbf{Topic} & \textbf{Category} & \textbf{CN} & \textbf{DR} \\
% \hline
% 2207227 & EC2 instance store stop & Implementation & 0.78 & 0.85 \\
% 2623773 & Azure blob large file upload & Implementation & 0.80 & 0.74 \\
% 2656068 & Sending email from web2py on GAE & Implementation & 0.91 & 0.82 \\
% 3408945 & GAE fastest object existence check & Implementation & 0.84 & 0.51 \\
% 4073627 & Mix-in properties in GAE model & Implementation & 0.89 & 0.57 \\
% 4913891 & GAE cron jobs specific version & Implementation & 0.79 & 0.78 \\
% 4942622 & Heroku database migration rollback & Implementation & 0.87 & 0.84 \\
% 5223763 & Ruby FTP without saving file & Implementation & 0.87 & 0.64 \\
% 9256008 & Azure package msbuild files & Implementation & 0.76 & 0.70 \\
% 9648559 & Heroku push behind proxy & Implementation & 0.82 & 0.87 \\
% 11343462 & App Engine send mail function & Implementation & 0.81 & 0.83 \\
% 12486730 & Azure Web Roles scaling threshold & Best Practice & 0.76 & 0.80 \\
% 15833039 & AWS Route 53 www redirect & Implementation & 0.79 & 0.79 \\
% 18267749 & Python App Engine Translate API error & Causal & 0.73 & 0.73 \\
% 18559099 & RDS SQL super privilege error & Implementation & 0.68 & 0.62 \\
% 22220837 & Hive partition by date & Implementation & 0.88 & 0.91 \\
% 27230199 & Azure File Storage SAS url & Implementation & 0.87 & 0.84 \\
% 29092758 & BigQuery median calculation & Implementation & 0.61 & 0.60 \\
% 31149501 & Docker container name resolution & Implementation & 0.67 & 0.63 \\
% 33377022 & Docker copy files to host & Implementation & 0.83 & 0.78 \\
% 34411293 & CloudFront reboot survival & Implementation & 0.64 & 0.53 \\
% 34740610 & GAE deploy pagespeed warning & Implementation & 0.88 & 0.83 \\
% 34800976 & CloudWatch alarm to Lambda & Implementation & 0.82 & 0.89 \\
% 35878619 & Lambda task frequency & Implementation & 0.84 & 0.87 \\
% 39138488 & Docker container conditional start & Implementation & 0.78 & 0.80 \\
% 39772259 & API Gateway Lambda proxy & Implementation & 0.81 & 0.82 \\
% 40335179 & Kubernetes PV resize & Implementation & 0.89 & 0.77 \\
% 40704760 & UserSecretsId attribute error & Implementation & 0.81 & 0.77 \\
% 40863417 & CORS issue Vue.js & Implementation & 0.63 & 0.61 \\
% 41404527 & Download S3 image to ImageView & Implementation & 0.70 & 0.74 \\
% 42545431 & Docker-Compose vs Docker-Swarm & Contrastive & 0.86 & 0.86 \\
% 46757614 & Firestore array update & Implementation & 0.89 & 0.86 \\
% 46797662 & Cloud Function version retrieval & Implementation & 0.73 & 0.75 \\
% 47710767 & Docker-compose v3 depends\_on & Implementation & 0.84 & 0.81 \\
% 48951840 & BigQuery Star Rating report & Implementation & 0.80 & 0.83 \\
% 50140885 & SAM vs CloudFormation template & Contrastive & 0.87 & 0.90 \\
% 50981864 & NLB to ALB traffic routing & Implementation & 0.82 & 0.83 \\
% 52026405 & Athena Boto3 DataFrame creation & Implementation & 0.84 & 0.86 \\
% 52309496 & Kubernetes Objects vs Resources & Contrastive & 0.71 & 0.71 \\
% 52759455 & VSTS parallel agent jobs & Implementation & 0.73 & 0.79 \\
% 53796032 & Lambda@Edge assumable role error & Causal & 0.87 & 0.84 \\
% 56386812 & Azure Pipeline Docker build failure & Implementation & 0.78 & 0.72 \\
% 57234110 & Azure DevOps Service Connections & Implementation & 0.72 & 0.67 \\
% 57764237 & Kubernetes Ingress external service & Implementation & 0.72 & 0.68 \\
% 57929675 & Kubernetes Ingress 503 error & Implementation & 0.74 & 0.81 \\
% 57945555 & Kubernetes utils Pod execution & Implementation & 0.81 & 0.83 \\
% 57975052 & Cross-account ECR access & Implementation & 0.66 & 0.71 \\
% 59132598 & Athena casting issue & Implementation & 0.73 & 0.56 \\
% 59465327 & Azure Pipeline HTML reports & Implementation & 0.82 & 0.83 \\
% 59931761 & Lambda unzipped size limit & Implementation & 0.81 & 0.82 \\
% 60113774 & Multiple Terraform versions & Implementation & 0.84 & 0.86 \\
% 60509976 & Azure build memory error & Implementation & 0.55 & 0.51 \\
% 61616203 & Nginx Ingress webhook failure & Implementation & 0.85 & 0.85 \\
% 62256761 & AWS Lambda response card C\# & Implementation & 0.68 & 0.69 \\
% 65035156 & Azure Pipeline PR trigger & Implementation & 0.63 & 0.64 \\
% 65180131 & Lambda singleton token coordination & Implementation & 0.83 & 0.81 \\
% 65993432 & Azure DevOps Pipeline empty zip & Implementation & 0.72 & 0.74 \\
% 71654588 & Heroku memory quota Selenium & Implementation & 0.73 & 0.67 \\
% 72874999 & Kubernetes CNI type detection & Implementation & 0.81 & 0.75 \\
% 73260296 & AWS VPC subnet traffic & Causal & 0.78 & 0.79 \\
% \hline
% \multicolumn{3}{|r|}{\textbf{Mean (SD)}} & \textbf{0.78 (0.08)} & \textbf{0.71 (0.12)} \\
% \hline
% \end{tabularx}
% \normalsize
% \end{table}


\section{Evaluation Run~History}

\label{app:eval_run_history}


Table~\ref{tab:eval-run-history} condenses the CloudNerd pipeline evolution documented in the project repository (\texttt{result/CLOUDNERD\_EVALUATION\_HISTORY.md}). Rows marked \emph{\hl{exclude} %MDPI: RESPONSE: Please leave as is.
} are retained for audit but must not be cited as primary evidence. Internal repository version labels appear only in this table for~reproducibility.

\paragraph{Reproducibility artifacts.}
Source code: %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”.
% Author response: Access date added to the URL on the next line in the standard MDPI format.
 \url{https://github.com/Harsh23Kashyap/Custom-Nerd} (accessed on 25 June 2026). Primary 120-question score exports and three-way comparison CSVs live under \texttt{result/runs-120/} in that repository \textls[-15]{(including \texttt{OtherResearch/dennis-three-way-primary-120.csv}). The CloudNerd--OpenDeepResearch concatenation diagnostic (Section~\ref{sec:combining}) is reproduced from \texttt{result/runs-120/OtherResearch/cn-odr-concat-primary-120/}} via \texttt{scripts/parse\_odr \_report.py}, \texttt{build\_concat\_120.py}, and~\texttt{aggregate\_concat\_results.py}. Benchmark questions: \texttt{stackoverflow\_with\_selection\_120\_v6.json} (Appendix~\ref{appendix:benchmark_construction}). Pin the repository commit used for submission when uploading to Overleaf or the journal data~archive.

\begin{table}[H]

\caption{Condensed CloudNerd evaluation run history. Primary result: CloudNerd on the 120-question~benchmark.}
\label{tab:eval-run-history}
\scriptsize
\renewcommand{\arraystretch}{1.15}
\setlength{\tabcolsep}{4pt}
\begin{tabularx}{\textwidth}{@{}>{\centering\arraybackslash}m{0.55cm} >{\raggedright\arraybackslash}X >{\raggedright\arraybackslash}m{2.4cm} >{\centering\arraybackslash}m{1.1cm} >{\raggedright\arraybackslash}m{2.5cm}@{}}
\toprule
\textbf{\#} & \textbf{Pipeline Step} & \textbf{Dataset} & \textbf{Scored} & \textbf{Role} \\
\midrule
1 & Legacy strict single-pass retrieval & Development & 5/20 & Floor~baseline \\
2 & Legacy relaxed single-pass retrieval & Development & 11/20 & Appendix~only \\
3 & Three-tier retrieval without citation recovery & Development & 6/20 & Main (motivation) \\
4 & Three-tier retrieval (stale checkpoint rerun) & Development & 20/20 & Exclude (stale checkpoint) \\
5 & Design smoke tests (diagnostic subset) & 2 Qs & --- & Appendix (design) \\
6 & Three-tier retrieval + citation recovery & Development & 20/20 & Main \\
7 & Three-tier retrieval + direct-answer synthesis prompt & Development & 19/20 & Main \\
8 & Evidence-constrained prompt (pre-deployment gate) & 9 Qs & --- & Appendix (gate) \\
9 & Strict grounding \& two-stage synthesis experiments & subsets & --- & Withdrawn \\
10 & CloudNerd (evaluated on development set) & Development & \textbf{\hl{20/20} %MDPI: RESPONSE: Please leave as is.
} & Main \\
11 & Alternative prompt with expanded supplemental retrieval & Development & 20/20 & Negative~result \\
12 & \textbf{CloudNerd (evaluated on test benchmark)} %MDPI: RESPONSE: Please leave as is.
% Author response: All four highlighted cells in this row are confirmed correct. CloudNerd was evaluated on the 120-question test benchmark with 120/120 questions scored, and this row is the Primary result of the paper. Numbers cross-checked against \texttt{result/runs-120/cascade-v1.5/eval\_120\_v1.5.csv} and Table~\ref{tab:three-way-aggregates-120}.
 & \textbf{120-question benchmark} %MDPI: RESPONSE: Please leave as is.
 & \textbf{120/120} %MDPI: RESPONSE: Please leave as is.
 & \textbf{Primary result} %MDPI: RESPONSE: Please leave as is.
 \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\textls[-25]{Tier-stratified development-set comparisons (direct-answer synthesis prompt vs.\ AutoGPT; winning retrieval strictness tier per question) appear in Section~\ref{sec:eval_devset_supp} (Tables~\ref{tab:devset-tier-autogpt} and~\ref{tab:devset-tier-faith-autogpt}).}

\begin{table}[H]

\caption{Key CloudNerd development-set runs (aggregate RAGAS and STS). Rows marked \emph{\hl{exclude} %MDPI: RESPONSE: Please leave as is.
} or \emph{\hl{withdrawn} %MDPI: RESPONSE: Please leave as is.
} are audit-only. Full narrative: \texttt{CLOUDNERD\_EVALUATION\_HISTORY\_DETAILED.md}.}
\label{tab:devset-key-runs}
\small
\setlength{\tabcolsep}{3pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{5cm} >{\centering\arraybackslash}m{1cm} *{4}{>{\centering\arraybackslash}m{1cm}} >{\raggedright\arraybackslash}m{3cm}@{}}
\toprule
\textbf{Run/Milestone} & \textbf{Scored} & \textbf{STS} & \textbf{Faith} & \textbf{Rel} & \textbf{Sim} & \textbf{Status} \\
\midrule
Legacy strict (single-pass) & 5/20 & 0.553 & 0.864 & 0.715 & 0.831 & Floor~baseline \\
Legacy relaxed (query fallback) & 11/20 & 0.624 & 0.633 & 0.725 & 0.847 & Appendix~only \\
Cascade, no citation recovery & 6/20 & 0.587 & 0.627 & 0.447 & 0.839 & Motivation \\
Cascade + citation recovery & 20/20 & 0.677 & 0.455 & 0.486 & 0.860 & Superseded \\
Direct-answer synthesis prompt & 19/20 & 0.668 & 0.500 & 0.701 & 0.862 & Superseded \\
%\textbf{\hl{CloudNerd (evaluated, dev set)} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{20/20} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.664} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.571} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.803} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{0.859} %MDPI: RESPONSE: Please leave as is.
%} & \textbf{\hl{Shipped} %MDPI: RESPONSE: Please leave as is.
%} \\
%Expanded supplemental (v1.6) & 20/20 & 0.663 & 0.562 & 0.753 & 0.856 & Failed~gate \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}


\begin{table}[H]\ContinuedFloat  

\caption{\emph{Cont.}}
\label{tab:devset-key-runs}
\small
\setlength{\tabcolsep}{3pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{5cm} >{\centering\arraybackslash}m{1cm} *{4}{>{\centering\arraybackslash}m{1cm}} >{\raggedright\arraybackslash}m{3cm}@{}}
\toprule
\textbf{Run/Milestone} & \textbf{Scored} & \textbf{STS} & \textbf{Faith} & \textbf{Rel} & \textbf{Sim} & \textbf{Status} \\
\midrule
%Legacy strict (single-pass) & 5/20 & 0.553 & 0.864 & 0.715 & 0.831 & Floor~baseline \\
%Legacy relaxed (query fallback) & 11/20 & 0.624 & 0.633 & 0.725 & 0.847 & Appendix~only \\
%Cascade, no citation recovery & 6/20 & 0.587 & 0.627 & 0.447 & 0.839 & Motivation \\
%Cascade + citation recovery & 20/20 & 0.677 & 0.455 & 0.486 & 0.860 & Superseded \\
%Direct-answer synthesis prompt & 19/20 & 0.668 & 0.500 & 0.701 & 0.862 & Superseded \\
\textbf{CloudNerd (evaluated, dev set)} %MDPI: RESPONSE: Please leave as is.
% Author response: All seven highlighted cells in this row are confirmed correct. CloudNerd shipped configuration on the 20-question development set: scored 20/20, STS=0.664, Faithfulness=0.571, Relevancy=0.803, Similarity=0.859. Status: Shipped. Numbers cross-checked against \texttt{result/setB\_runs/cascade-v1.5/eval\_setB.csv}.
 & \textbf{20/20} %MDPI: RESPONSE: Please leave as is.
 & \textbf{0.664} %MDPI: RESPONSE: Please leave as is.
 & \textbf{0.571} %MDPI: RESPONSE: Please leave as is.
 & \textbf{0.803} %MDPI: RESPONSE: Please leave as is.
 & \textbf{0.859} %MDPI: RESPONSE: Please leave as is.
 & \textbf{Shipped} %MDPI: RESPONSE: Please leave as is.
 \\
Expanded supplemental (v1.6) & 20/20 & 0.663 & 0.562 & 0.753 & 0.856 & Failed~gate \\
\bottomrule
\end{tabularx}
\normalsize
\end{table}

\vspace{-6pt}
\begin{table}[H]

\caption{Prompt evolution during CloudNerd development (development-set outcomes unless noted). \emph{\hl{Shipped} %MDPI: RESPONSE: Please leave as is.
} configuration: \texttt{openai\_prompts\_combined\_v1\_5.py}.}
\label{tab:prompt-evolution}
\small
\setlength{\tabcolsep}{3pt}
\renewcommand{\arraystretch}{1.12}
\begin{tabularx}{\textwidth}{@{}>{\raggedright\arraybackslash}m{2.2cm} >{\raggedright\arraybackslash}X >{\centering\arraybackslash}m{0.75cm} >{\centering\arraybackslash}m{1cm} >{\raggedright\arraybackslash}m{2.5cm}@{}}
\toprule
\textbf{Version} & \textbf{Key Change} & \textbf{Rel} & \textbf{Faith} & \textbf{Outcome} \\
\midrule
Baseline & Strict relevance classifier; original synthesis prompt & poor & 0.864 $^\dagger$ & 5/20~scored \\
Citation-v1 & Softer classifier (Docker/k8s/Terraform posts) & 0.486 & 0.455 & First 20/20 \\
Combined & Citation-v1 + direct-answer final prompt & 0.701 & 0.500 & Relevancy~jump \\
Faithfulness-v2 & Strict grounding synthesis & 0.335 & 0.454 & Withdrawn (subset) \\
Two-pass v2 & Verbatim fact-binding second stage & 0.323 & 0.880 & Withdrawn (subset) \\
\textbf{\hl{Combined-v1.5} %MDPI: RESPONSE: Please leave as is.
} & + four evidence-constrained answer rules & \textbf{\hl{0.803} %MDPI: RESPONSE: Please leave as is.
} & \textbf{\hl{0.571} %MDPI: RESPONSE: Please leave as is.
} & \textbf{\hl{Shipped} %MDPI: RESPONSE: Please leave as is.
} \\
Combined-v1.6 & + actionable-first + expanded gap-fill & 0.753 & 0.562 & Failed~gate \\
\bottomrule
\end{tabularx}


\noindent{\footnotesize{$^\dagger$ Faithfulness on the five scored legacy-strict questions only.}}


\end{table}



\section{Withdrawn Faithfulness-Oriented~Configurations}
\label{app:rejected_faithfulness}


Strict grounding and two-stage synthesis experiments on problem-question subsets raised faithfulness on difficult cases but collapsed answer relevancy; they were not deployed. Section~\ref{sec:ablation_hallucination} and Table~\ref{tab:rejected-faithfulness} in the main text summarize the design rationale and~scores.

\section{Statistical Methodology for Paired System~Comparison}\label{statistical-methodology}


Because each test question was evaluated by both systems, we used a non-parametric paired permutation test to assess whether CloudNerd's scores significantly differ from OpenDeepResearch or AutoGPT scores following the methodology of~\cite{shasha2010statistics}. Comparisons against OpenDeepResearch and AutoGPT use $n = 120$ questions on the test benchmark; development-set component comparisons use $n = 20$. Confidence intervals are reported at the 95\% level to match the deliverables in Tables~\ref{tab:cloudnerd-vs-odr-120} and~\ref{tab:cloudnerd-vs-autogpt-120}.

\paragraph{RAGAS metrics.}
Faithfulness measures whether claims in the generated answer are supported by retrieved contexts. Answer similarity compares semantic match to the gold accepted answer. Answer relevancy measures whether the answer addresses the user question. The~RAGAS geometric mean is the mean over questions of $\sqrt[3]{\text{faith}\times\text{sim}\times\text{rel}}$ (computed only when all three RAGAS scores are positive). We report RAGAS alongside STS because high embedding similarity cannot detect a single critical technical error (Section~\ref{sec:eval_limitations}).

\paragraph{Permutation Test for p-value}

Algorithm~\ref{alg:pval} {presents} 
 the permutation test procedure in pseudocode, following the implementation from the Statistics is Easy! methodology~\cite{shasha2010statistics}.

This one-sided permutation test uses a sign-flipping approach to evaluate the null hypothesis that there is no systematic difference between CloudNerd and the baseline system. For~each iteration, the~test randomly reverses the sign of each paired difference (equivalent to swapping which system gets credit for the performance). The~test counts how often these random sign flips produce a sum of differences larger than the observed sum. A~small $p$-value indicates that the observed difference is unlikely to occur by chance if both systems performed~equivalently.

\vspace{9pt}
\setcounter{algocf}{0}

\renewcommand\thealgocf{A\arabic{algocf}}

\setlength{\algotitleheightrule}{0.5pt}
\begin{algorithm}[H]
\caption{\hl{Paired} %MDPI: It should be Algorithm A1, we revise it. RESPONSE: confirmed.
 Permutation Test for $p$-value (Sign-Flip Method)}
\label{alg:pval}
\SetAlgoLined
\DontPrintSemicolon

\KwIn{$S_{\text{CloudNerd}} = \{s_1^C, \ldots, s_{120}^C\}$, $S_{\text{Baseline}} = \{s_1^B, \ldots, s_{120}^B\}$ (paired scores on the same questions)}
\KwOut{$p$-value}
\BlankLine

Compute observed sum of differences: $d \gets \sum_{i=1}^{120}(s_i^C - s_i^B)$\;

\eIf{$d < 0$}{
    $S_{\text{big}} \gets S_{\text{Baseline}}$\;
    $S_{\text{small}} \gets S_{\text{CloudNerd}}$\;
    $d \gets -d$ \tcp*{Make d positive}
}{
    $S_{\text{big}} \gets S_{\text{CloudNerd}}$\;
    $S_{\text{small}} \gets S_{\text{Baseline}}$\;
}

count $\gets 0$\;
\BlankLine

\For{iter $= 1$ to $10{,}000$}{
    $d^* \gets 0$\;
    
    \For{$i = 1$ to $120$}{
        $r \gets$ random number from $[0, 1]$\;
        \eIf{$r < 0.5$}{
            $d^* \gets d^* + (s_i^{\text{small}} - s_i^{\text{big}})$ \tcp*{Negative contribution}
        }{
            $d^* \gets d^* + (s_i^{\text{big}} - s_i^{\text{small}})$ \tcp*{Positive contribution}
        }
    }
    
    \If{$d^* > d$}{
        count $\gets$ count $+ 1$\;
    }
}
\BlankLine

\KwRet{$p\text{-value} = \text{count} / 10{,}000$}
\vspace{1em}
\end{algorithm}




\paragraph{Confidence Interval}

Algorithm~\ref{alg:ci} \hl{presents} %MDPI: It should be Algorithm A2, please check and revise it. RESPONSE: done
 the bootstrap confidence interval procedure in pseudocode, following the bias-corrected percentile method from Statistics is Easy!~\cite{shasha2010statistics}. The~confidence level is set to 95\%.

The bias-corrected bootstrap method adjusts the confidence interval bounds when the bootstrap distribution is asymmetric. The~correction works as follows:

\begin{itemize}
  \item If the proportion of bootstrap means below the observed mean is 0.5, the~distribution is symmetric and the bias-corrected interval equals the standard~interval.
  
  \item If the proportion is less than 0.5, the~observed mean is below the bootstrap distribution center, so the bounds shift~left.
  
  \item If the proportion is greater than 0.5, the~observed mean is above the bootstrap distribution center, so the bounds shift right.
\end{itemize}

The adjustment ensures that the confidence interval properly accounts for skewness in the bootstrap distribution, providing more accurate coverage than the standard percentile method when the underlying distribution is~asymmetric.

\vspace{6pt}
\begin{algorithm}[H]
\small
% --- Preamble Settings ---
\SetAlgoLined
\DontPrintSemicolon
\SetCustomAlgoRuledWidth{0.4pt} % Adjust thickness if desired

\caption{\hl{Bootstrap} %MDPI: It should be Algorithm A2, we revise it. RESPONSE: confirmed.
 Bias-Corrected Confidence~Interval}
\label{alg:ci}
\vspace{0.8em} % Gap between title and input

\KwIn{CloudNerd scores, baseline scores, number of bootstrap iterations $B$}
\KwOut{Standard and bias-corrected 95\% confidence intervals}
\BlankLine

\tcp{Step 1: Compute differences}
\For{each question $i$}{
    $\text{sample}[i] \gets \text{CloudNerd}[i] - \text{Baseline}[i]$\;
}
$\text{observed\_mean} \gets \text{mean of sample}$\;
\BlankLine

\tcp{Step 2: Bootstrap resampling}
$\text{out} \gets \text{empty list}$\;
$\text{num\_below\_observed} \gets 0$\;
\BlankLine

\For{$\text{iteration} = 1$ \KwTo $B$}{
    Randomly sample from $\text{sample}$ array with replacement to create $\text{bootstrap\_sample}$\;
    
    $\text{boot\_mean} \gets \text{mean of bootstrap\_sample}$\;
    
    Add $\text{boot\_mean}$ to $\text{out}$ list\;
    
    \If{$\text{boot\_mean} < \text{observed\_mean}$}{
        $\text{num\_below\_observed} \gets \text{num\_below\_observed} + 1$\;
    }
}
\BlankLine

Sort $\text{out}$ list in ascending order\;
\BlankLine

\tcp{Step 3: Standard 95\% Confidence Interval}
$\text{tails} \gets (1 - 0.95) / 2$\;
$\text{lower\_bound} \gets \lceil B \times \text{tails} \rceil$\;
$\text{upper\_bound} \gets \lfloor B \times (1 - \text{tails}) \rfloor$\;
\BlankLine

\tcp{Step 4: Bias-Corrected 95\% Confidence Interval}
$p \gets \text{num\_below\_observed} / B$\;
$\text{dist\_from\_center} \gets p - 0.5$\;
$z_0 \gets \text{convert } \text{dist\_from\_center} \text{ to z-score using normal distribution table}$\;
\BlankLine

$\text{tail\_sds} \gets \text{convert } 0.45 \text{ to z-score using normal distribution table}$\;
$z_{\alpha/2} \gets 0 - \text{tail\_sds}$\;
$z_{1-\alpha/2} \gets \text{tail\_sds}$\;
\BlankLine

$\text{adj\_lower\_proportion} \gets 0.5 + \text{convert } (z_{\alpha/2} + 2 \times z_0) \text{ back to proportion}$\;
$\text{adj\_upper\_proportion} \gets 0.5 + \text{convert } (z_{1-\alpha/2} + 2 \times z_0) \text{ back to proportion}$\;
\BlankLine

$\text{bias\_corr\_lower\_bound} \gets \lceil B \times \text{adj\_lower\_proportion} \rceil$\;
$\text{bias\_corr\_upper\_bound} \gets \lfloor B \times \text{adj\_upper\_proportion} \rfloor$\;
\vspace{1em} % Gap at bottom
\end{algorithm}
\vspace{6pt}



\paragraph{Statistical Interpretation}
When the $p$-value is less than $\alpha = 0.05$, we reject the null hypothesis and conclude that the two systems perform significantly differently. If~the 95\% bias-corrected confidence interval for the mean difference excludes zero, this provides evidence that the effect is likely to hold across the~benchmark.
\section{Paired Bootstrap Test~Implementation}
\label{appendix:bootstrap_code}

The following Python implementation performs paired bootstrap hypothesis testing with bias-corrected confidence intervals. This code was used for CloudNerd vs.\ OpenDeepResearch and AutoGPT comparisons in Section~\ref{sec:NerdEngine_evaluation} (Tables~\ref{tab:cloudnerd-vs-odr-120} and~\ref{tab:cloudnerd-vs-autogpt-120}).

%\textbf{\textcolor{red}{TBD --- Full \texttt{area\_to\_sd} lookup table omitted for brevity below; verify against source code before submission.}}

\subsection{Key~Functions}

\begin{itemize}
  \item \texttt{bootstrap(x)}: Generates a bootstrap sample by sampling with replacement from list~\texttt{x}
  
  \item \texttt{mean(grp)}: Computes the arithmetic mean of a~list
  
  \item \texttt{pairedtestpval(n1, n2, nof\_iters)}: Computes the two-tailed $p$-value for paired differences using randomization~test
  
  \item \texttt{pairedtestconf(n1, n2, nof\_iters)}: Computes both standard and bias-corrected bootstrap confidence intervals for the mean~difference
  
  \item \texttt{area\_to\_sd(area)}: Converts proportion of area under normal curve to standard deviations (for bias correction)
  
  \item \texttt{sd\_to\_area(sd)}: Converts standard deviations to proportion of area under normal curve (for bias correction)
\end{itemize}

\subsection{Usage~Example}

\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
# CloudNerd scores (n1) and baseline scores (n2), paired by question
n1 = [0.78, 0.8, 0.91, ...]  # 120 paired comparisons
n2 = [0.85, 0.69, 0.82, ...]  # 120 paired~comparisons

# Compute p-value (10,000 iterations)
pval = pairedtestpval(n1, n2, 10000)

# Compute confidence intervals (10,000 iterations)
pairedtestconf(n1, n2, 10000)
\end{lstlisting}

\subsection{Implementation}

The full implementation is provided below. The~code uses the bias-corrected percentile method to adjust confidence interval bounds when the bootstrap distribution is~skewed.

\vspace{6pt}
\begin{lstlisting}[language=python,xleftmargin=0.5em, xrightmargin=0.5em]
import random
import~math

nof_iters = 10000
conf_interval = 0.9

# Standard normal CDF lookup table
area_to_sd_map = [0.0000, 0.0040, 0.0080, 0.0120, 0.0160, 0.0199, 
    0.0239, 0.0279, 0.0319, 0.0359, 0.0398, 0.0438, 0.0478, 0.0517]
    # ... (full table omitted for brevity in document)

def sd_to_area(sd):
    """Convert z-score to proportion of area under normal curve"""
    sign = 1
    if sd < 0:
        sign = -1
    sd = math.fabs(sd)
    index = int(sd * 100)
    if len(area_to_sd_map) <= index:
        return sign * area_to_sd_map[-1]
    if index == (sd * 100):
        return sign * area_to_sd_map[index]
    return sign * (area_to_sd_map[index] + area_to_sd_map[index + 1]) / 2

def area_to_sd(area):
    """Convert proportion of area to z-score"""
    sign = 1
    if area < 0:
        sign = -1
    area = math.fabs(area)
    for a in range(len(area_to_sd_map)):
        if area == area_to_sd_map[a]:
            return sign * a / 100
        if 0 < a and area_to_sd_map[a - 1] < area < area_to_sd_map[a]:
            return sign * (a - 0.5) / 100
    return sign * (len(area_to_sd_map) - 1) / 100

def bootstrap(x):
    """Generate bootstrap sample by sampling with replacement"""
    samp_x = []
    for i in range(len(x)):
        samp_x.append(random.choice(x))
    return~samp_x

def mean(grp):
    """Compute arithmetic mean"""
    return sum(grp) / float(len(grp))

def pairedtestpval(n1, n2, nof_iters):
    """Compute two-tailed p-value for paired test"""
    ndiff = sum([n1[i] - n2[i] for i in range(len(n1))])
    if ndiff < 0:
        nbig = n2
        nsmall = n1
        ndiff = -ndiff
    else:
        nbig = n1
        nsmall = n2
    
    bcount = 0
    for niter in range(nof_iters):
        tdiff = 0
        for i in range(len(nbig)):
            a = random.random()
            if a < 0.5:
                tdiff += nsmall[i] - nbig[i]
            else:
                tdiff += nbig[i] - nsmall[i]
        if tdiff > ndiff:
            bcount += 1
    return bcount / nof_iters

def pairedtestconf(n1, n2, nof_iters):
    """Compute standard and bias-corrected bootstrap CIs"""
    sample = [n1[i] - n2[i] for i in range(len(n1))]
    observed_mean = mean(sample)
    
    num_below_observed = 0
    out = []
    
    # Generate bootstrap samples
    for i in range(nof_iters):
        boot_mean = mean(bootstrap(sample))
        if boot_mean < observed_mean:
            num_below_observed += 1
        out.append(boot_mean)
    
    out.sort()
    
    # Standard confidence interval
    tails = (1 - conf_interval) / 2
    lower_bound = int(math.ceil(nof_iters * tails))
    upper_bound = int(math.floor(nof_iters * (1 - tails)))
    
    # Bias-corrected confidence interval
    p = num_below_observed / float(nof_iters)
    dist_from_center = p - 0.5
    z_0 = area_to_sd(dist_from_center)
    
    tail_sds = area_to_sd(conf_interval / 2)
    z_alpha_over_2 = 0 - tail_sds
    z_1_minus_alpha_over_2 = tail_sds
    
    bias_corr_lower_bound = int(math.ceil(
        nof_iters * (0.5 + sd_to_area(z_alpha_over_2 + (2 * z_0)))
    ))
    bias_corr_upper_bound = int(math.floor(
        nof_iters * (0.5 + sd_to_area(z_1_minus_alpha_over_2 + (2 * z_0)))
    ))
    
    # Print results
    print("Observed mean: %.4f" % observed_mean)
    print("Standard 95%% CI: [%.4f, %.4f]" % 
          (out[lower_bound], out[upper_bound]))
    print("Bias-corrected 95%% CI: [%.4f, %.4f]" % 
          (out[bias_corr_lower_bound], out[bias_corr_upper_bound]))
\end{lstlisting}

\subsection{Statistical~Interpretation}

The bias-corrected percentile method adjusts for skewness in the bootstrap distribution by computing a bias-correction factor $z_0$ based on the proportion of bootstrap samples below the observed statistic. This correction shifts the confidence interval bounds to account for asymmetry, providing more accurate coverage when the sampling distribution is~non-normal.

For the CloudNerd vs.\ OpenDeepResearch STS comparison on the 120-question benchmark (Table~\ref{tab:cloudnerd-vs-odr-120}):
\begin{itemize}
  \item The observed mean difference was $\bar{\Delta} = +0.024$
  \item The 95\% bootstrap confidence interval was [+0.006, +0.042]
  \item The paired permutation test $p$-value was 0.011
  \item The interval excludes zero, confirming statistical significance at $\alpha = 0.05$
\end{itemize}


% \section{Test Questions}
% \label{appendix:test_questions}

% Here is the full text of the questions from the comparison test.

% \begin{enumerate}[leftmargin=1.5em]
%   \item \textbf{ID.2207227} What happens to EC2 instance when it is stopped with instance store?
%   \item \textbf{ID.2623773} How to upload large files to Azure Blob Storage?
%   \item \textbf{ID.2656068} How do I send email from a web2py application on Google App Engine?
%   \item \textbf{ID.3408945} What is the fastest way to check object existence in Google App Engine?
%   \item \textbf{ID.4073627} How to use mix-in properties in Google App Engine model?
%   \item \textbf{ID.4913891} How to run GAE cron jobs on a specific version?
%   \item \textbf{ID.4942622} How to rollback database migration in Heroku?
%   \item \textbf{ID.5223763} How to use Ruby FTP without saving file to disk?
%   \item \textbf{ID.9256008} What files should be included in Azure package with msbuild?
%   \item \textbf{ID.9648559} How to push to Heroku when behind a proxy?
%   \item \textbf{ID.11343462} How does App Engine send mail function work?
%   \item \textbf{ID.12486730} What is the best practice for Azure Web Roles scaling threshold?
%   \item \textbf{ID.15833039} How to redirect www to non-www in AWS Route 53?
%   \item \textbf{ID.18267749} Why does Python App Engine Translate API error occur?
%   \item \textbf{ID.18559099} How to fix RDS SQL super privilege error?
%   \item \textbf{ID.22220837} How to partition by date in Hive?
%   \item \textbf{ID.27230199} How to generate SAS url for Azure File Storage?
%   \item \textbf{ID.29092758} How to calculate median in BigQuery?
%   \item \textbf{ID.31149501} How to resolve Docker container names?
%   \item \textbf{ID.33377022} How to copy files from Docker container to host?
%   \item \textbf{ID.34411293} Does CloudFront configuration survive reboot?
%   \item \textbf{ID.34740610} How to fix GAE deployment PageSpeed warning?
%   \item \textbf{ID.34800976} How to trigger Lambda from CloudWatch alarm?
%   \item \textbf{ID.35878619} What is the maximum frequency for Lambda tasks?
%   \item \textbf{ID.39138488} How to start Docker container conditionally?
%   \item \textbf{ID.39772259} How to configure API Gateway Lambda proxy integration?
%   \item \textbf{ID.40335179} How to resize Kubernetes persistent volume?
%   \item \textbf{ID.40704760} How to fix UserSecretsId attribute error?
%   \item \textbf{ID.40863417} How to resolve CORS issue in Vue.js application?
%   \item \textbf{ID.41404527} How to download S3 image to ImageView?
%   \item \textbf{ID.42545431} What is the difference between Docker-Compose and Docker-Swarm?
%   \item \textbf{ID.46757614} How to update array field in Firestore?
%   \item \textbf{ID.46797662} How to retrieve Cloud Function version information?
%   \item \textbf{ID.47710767} How does depends\_on work in Docker-compose v3?
%   \item \textbf{ID.48951840} How to create Star Rating report in BigQuery?
%   \item \textbf{ID.50140885} What is the difference between SAM and CloudFormation template?
%   \item \textbf{ID.50981864} How to route traffic from NLB to ALB?
%   \item \textbf{ID.52026405} How to create DataFrame from Athena query using Boto3?
%   \item \textbf{ID.52309496} What is the difference between Kubernetes Objects and Resources?
%   \item \textbf{ID.52759455} How to configure parallel agent jobs in VSTS?
%   \item \textbf{ID.53796032} Why does Lambda@Edge throw assumable role error?
%   \item \textbf{ID.56386812} How to fix Azure Pipeline Docker build failure?
%   \item \textbf{ID.57234110} How to configure Azure DevOps Service Connections?
%   \item \textbf{ID.57764237} How to configure Kubernetes Ingress for external service?
%   \item \textbf{ID.57929675} How to fix Kubernetes Ingress 503 error?
%   \item \textbf{ID.57945555} How to execute commands in Kubernetes utils Pod?
%   \item \textbf{ID.57975052} How to configure cross-account ECR access?
%   \item \textbf{ID.59132598} How to fix Athena data type casting issue?
%   \item \textbf{ID.59465327} How to publish HTML reports in Azure Pipeline?
%   \item \textbf{ID.59931761} What is the Lambda unzipped deployment package size limit?
%   \item \textbf{ID.60113774} How to use multiple Terraform versions?
%   \item \textbf{ID.60509976} How to fix Azure build memory error?
%   \item \textbf{ID.61616203} How to fix Nginx Ingress webhook failure?
%   \item \textbf{ID.62256761} How to create response card in AWS Lambda using C\#?
%   \item \textbf{ID.65035156} How to configure Azure Pipeline PR trigger?
%   \item \textbf{ID.65180131} How to handle Lambda singleton with token coordination?
%   \item \textbf{ID.65993432} Why is Azure DevOps Pipeline creating empty zip?
%   \item \textbf{ID.71654588} How to fix Heroku memory quota error with Selenium?
%   \item \textbf{ID.72874999} How to detect Kubernetes CNI type?
%   \item \textbf{ID.73260296} Why does traffic not route between AWS VPC subnets?
% \end{enumerate}

% \begin{lstlisting}[title={\texttt{compress\_research\_system\_prompt  }}]
% You are a research assistant compressing research findings.

% IMPORTANT: You MUST preserve ALL Post ID numbers from the tool results.
% For context, today's date is {date}.

% Your task:
% 1. Review all the tool call results above
% 2. Find ALL Stack Overflow Post IDs mentioned (they look like "Post 27303346" 
%    or "VALID POST ID TO CITE: 30506833")
% 3. Create a clean summary that tracks which Post IDs contain relevant information
% 4. Present information naturally WITHOUT inline citations like "Post XXXXX states..."

% Output Format:

% **Search Queries Executed:**
% [List the queries]

% **Findings:**
% [Your summary presenting the information clearly, without inline Post ID citations]
% - Synthesize information from multiple posts into a coherent narrative
% - Focus on the content, not on which post it came from

% **Post IDs Used:**
% - Post 27303346: Brief description of what this post covered
% - Post 30506833: Brief description of what this post covered

% CRITICAL:
% - Extract all Post ID numbers from the tool results
% - Present findings naturally without saying "Post XXXXX says..." in the main text
% - List all Post IDs at the end with descriptions
% - Do NOT invent new Post ID numbers
% \end{lstlisting}

 




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{adjustwidth}{-\extralength}{0cm}

\begin{thebibliography}{999}

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\bibitem[Liu(2022)]{liu_llamaindex_2022}
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% \bibitem[Nguyen et~al.(2025)Nguyen, Chin, and Tai]{nguyen2025marag}
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\newblock {MA-RAG: A Multi-Agent Framework for Retrieval-Augmented Generation}.
\newblock \emph{arXiv} \textbf{2025}, arXiv:2505.20096.
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\newblock In Proceedings of the 63rd Annual Meeting of the
  Association for Computational Linguistics, \hl{Vienna, Austria, 27 July 27--1 August 2025;} %MDPI: Newly added location and date information. Please confirm. RESPONSE: confirmed.
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  3551--3578.
\newblock {\url{https://doi.org/10.18653/v1/2025.acl-long.179}}.

\bibitem[{LlamaIndex}(2025)]{llamaindex_workflows_2025}
{LlamaIndex}.
\newblock {\em Agentic Document Workflows: A Practical Guide};
\newblock Blog Post;
\newblock Agentic Document Workflows (ADW) Reference Architecture; \hl{LlamaIndex: San Francisco, CA, USA,} %MDPI: We added the name of the publisher and their location. Please confirm. RESPONSE: confirmed.
 2025.

\bibitem[Greyling(2024)]{greyling2024llamaindex}
Greyling, C.
\newblock {\em Multi-Document Agentic {RAG} with LlamaIndex};
\newblock GitHub Notebook;
\newblock LlamaIndex Multi-Document Agent Architecture Reference; \hl{LlamaIndex: San Francisco, CA, USA,} %MDPI: We added the name of the publisher and their location. Please confirm. RESPONSE: confirmed.
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Wu, S.; Yacub, Z.; Shasha, D.
\newblock {DietNerd: A Nutrition Question-Answering System That Summarizes and
  Evaluates Peer-Reviewed Scientific Articles}.
\newblock {\em Appl. Sci.} {\bf 2024}, {\em 14},~9021.
\newblock {\url{https://doi.org/10.3390/app14199021}}.

\bibitem[Hu et~al.(2025)Hu, Xuan, Zhou, Li, Li, Hu, and Fang]{hu2025agentic}
Hu, Y.; Xuan, W.; Zhou, Q.; Li, Z.; Li, Y.; Hu, J.; Fang, F.
\newblock {A Self-Correcting Agentic Graph {RAG} for Clinical Decision Support
  in Hepatology}.
\newblock {\em Front. Med.} {\bf 2025}, {\em 12}, \hl{1716327.} %MDPI: Newly added page information. Please confirm. RESPONSE: confirmed.
\newblock {\url{https://doi.org/10.3389/fmed.2025.1716327}}.

\bibitem[Prabha et~al.(2025)Prabha, Gomez-Cabello, Haider, Genovese, Trabilsy,
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Prabha, S.; Gomez-Cabello, C.A.; Haider, S.A.; Genovese, A.; Trabilsy, M.;
  Wood, N.G.; Bagaria, S.; Tao, C.; Forte, A.J.
\newblock {Enhancing Clinical Decision Support with Adaptive Iterative
  Self-Query Retrieval for Retrieval-Augmented Large Language Models}.
\newblock {\em Bioengineering} {\bf 2025}, {\em 12},~895.
\newblock {\url{https://doi.org/10.3390/bioengineering12080895}}.

% \bibitem[Li et~al.(2025)Li, Chen, Hu, Ai, Chen, Yang, Yang, Wu, Liu, and
%   Liu]{li2025lexrag}
% Li, H.; Chen, Y.; Hu, Y.; Ai, Q.; Chen, J.; Yang, X.; Yang, J.; Wu, Y.; Liu,
%   Z.; Liu, Y.
% \newblock {LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn
%   Legal Consultation Conversation}. In {\em \hl{Proceedings} of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval}; %MDPI: Newly added book information. Please confirm.
%  \hl{2025.} %MDPI: We are sorry but we could not find the required information about this entry. Please add the name of the publisher and their location.
%    Available online: \url{http://arxiv.org/abs/2502.20640} (last accessed in May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”.

\bibitem[Li et~al.(2025)Li, Chen, Hu, Ai, Chen, Yang, Yang, Wu, Liu, and Liu]{li2025lexrag}
Li, H.; Chen, Y.; Hu, Y.; Ai, Q.; Chen, J.; Yang, X.; Yang, J.; Wu, Y.; Liu, Z.; Liu, Y.
\newblock {LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation}.
\newblock In \emph{Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval};
ACM: New York, NY, USA, 2025; pp. 3606--3615.
\newblock \url{https://doi.org/10.1145/3726302.3730340}.
\newblock Available online: \url{http://arxiv.org/abs/2502.20640} (accessed on 27 May 2026).

% \bibitem[Zhao et~al.(2025)Zhao, Liu, Yang, and Miao]{li2025medrgag}
% Zhao, X.; Liu, S.; Yang, S.Y.; Miao, C.
% \newblock {MedRAG: Enhancing Retrieval-augmented Generation with Knowledge
%   Graph-Elicited Reasoning for Healthcare Copilot}.  In {\em \hl{Proceedings} of the ACM on Web Conference}; %MDPI: Newly added book information. Please confirm.
%  \hl{2025.} %MDPI: We are sorry but we could not find the required information about this entry. Please add the name of the publisher and their location.
%    Available online: \url{http://arxiv.org/abs/2502.04413} (last accessed in May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”

\bibitem[Zhao et~al.(2025)Zhao, Liu, Yang, and Miao]{li2025medrgag}
Zhao, X.; Liu, S.; Yang, S.-Y.; Miao, C.
\newblock {MedRAG: Enhancing Retrieval-Augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot}.
\newblock In \emph{WWW '25: Proceedings of the ACM on Web Conference 2025};
ACM: New York, NY, USA, 2025; pp. 4442--4457.
\newblock \url{https://doi.org/10.1145/3696410.3714782}.
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% \bibitem[Yang et~al.(2025)Yang, Ren, Opoku, Li, Ren, and Xing]{yang2025dsrag}
% Yang, M.; Ren, Y.; Opoku, D.O.; Li, R.; Ren, P.; Xing, C.
% \newblock {DSRAG: A Domain-Specific Retrieval Framework Based on
%   Document-derived Multimodal Knowledge Graph}. In {\em \hl{International} Conference on Web Information Systems and Applications}; Springer: Singapore, 2025. %MDPI: Newly added book, publisher and location information. Please confirm.
%    Available online: \url{http://arxiv.org/abs/2509.10467} (last accessed in May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”.

\bibitem[Yang et~al.(2025)Yang, Ren, Opoku, Li, Ren, and Xing]{yang2025dsrag}
Yang, M.; Ren, Y.; Opoku, D.O.; Li, R.; Ren, P.; Xing, C.
\newblock {DSRAG: A Domain-Specific Retrieval Framework Based on Document-Derived Multimodal Knowledge Graph}.
\newblock In \emph{Web Information Systems and Applications: 22nd International Conference, WISA 2025, Xi'an, China, October 1, 2025, Proceedings};
Springer: Singapore, 2025; pp. 523--530.
\newblock \url{https://doi.org/10.1007/978-981-95-6426-2_46}.
\newblock Available online: \url{http://arxiv.org/abs/2509.10467} (accessed on 27 May 2026).

% \bibitem[{Significant Gravitas}(2023)]{SignificantGravitas_AutoGPT_2023}
% {Significant Gravitas}.
% \newblock {\em AutoGPT: An Autonomous GPT-4 Experiment};
% \newblock Software;  \hl{2023.} %MDPI: We are sorry but we could not find the required information about this entry. Please add the name of the publisher and their location.

\bibitem[Significant Gravitas(2023)]{SignificantGravitas_AutoGPT_2023}
Significant Gravitas.
\newblock {AutoGPT: Build, Deploy, and Run AI Agents}.
\newblock Software; GitHub repository, 2023.
\newblock Available online: \url{https://github.com/Significant-Gravitas/AutoGPT} (accessed on 27 May 2026).


% \bibitem[Moura(2024)]{Moura_CrewAI_2024}
% Moura, J.
% \newblock {\em CrewAI: Framework for Orchestrating Role-Playing, Autonomous AI
%   Agents};
% \newblock Software;  \hl{2024.} %MDPI: We are sorry but we could not find the required information about this entry. Please add the name of the publisher and their location.

\bibitem[Moura(2024)]{Moura_CrewAI_2024}
Moura, J.
\newblock {CrewAI: Framework for Orchestrating Role-Playing, Autonomous AI Agents}.
\newblock Software; GitHub repository, 2024.
\newblock Available online: \url{https://github.com/crewAIInc/crewAI} (accessed on 27 May 2026).


% \bibitem[{LangChain}(2025)]{langchain_odr_2025}
% {LangChain}.
% \newblock {\em Open Deep Research}; LangGraph Supervisor--Researcher--Writer--Critic Baseline;
% \newblock Software;  \hl{2025.} %MDPI: We are sorry but we could not find the required information about this entry. Please add the name of the publisher and their location.

\bibitem[LangChain(2025)]{langchain_odr_2025}
LangChain.
\newblock {Open Deep Research}.
\newblock Software; GitHub repository, 2025.
\newblock Available online: \url{https://github.com/langchain-ai/open_deep_research} (accessed on 27 May 2026).

% \bibitem[Dang et~al.(2025)Dang, Qian, Luo, Fan, Xie, Shi, Chen, Yang, Che,
%   Tian, Xiong, Han, Liu, and Sun]{dang2025multi}
% Dang, Y.; Qian, C.; Luo, X.; Fan, J.; Xie, Z.; Shi, R.; Chen, W.; Yang, C.;
%   Che, X.; Tian, Y.;  et~al.
% \newblock {Multi-Agent Collaboration via Evolving Orchestration}. \hl{NeurIPS 2025; Puppeteer orchestration paradigm.} %MDPI: Please confirm if it is extra contents can be removed.
%  {\em \hl{Adv. Neural} Inf. Process. Syst.}  \textbf{2025}, {\em 38}, 165025--165059. %MDPI: We added journal source, volume and page number, please check and confirm.
%   Available online: \url{http://arxiv.org/abs/2505.19591} (last accessed in May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”.

\bibitem[Dang et~al.(2025)Dang, Qian, Luo, Fan, Xie, Shi, Chen, Yang, Che, Tian, Xiong, Han, Liu, and Sun]{dang2025multi}
Dang, Y.; Qian, C.; Luo, X.; Fan, J.; Xie, Z.; Shi, R.; Chen, W.; Yang, C.; Che, X.; Tian, Y.; Xiong, X.; Han, L.; Liu, Z.; Sun, M.
\newblock {Multi-Agent Collaboration via Evolving Orchestration}.
\newblock \emph{Advances in Neural Information Processing Systems} \textbf{2025}, \emph{38}, 165025--165059.
\newblock Available online: \url{https://proceedings.neurips.cc/paper_files/paper/2025/hash/f1320d2e2842169c6fc89dcbd80e94d0-Abstract-Conference.html} (accessed on 28 May 2026).


% \bibitem[Zhang et~al.(2025)Zhang, Fan, Cai, Sun, and Wang]{zhang2025osc}
% Zhang, J.; Fan, Y.; Cai, K.; Sun, X.; Wang, K.
% \newblock {OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in
%   Multi-Agent {LLM} Collaboration}. \hl{EMNLP 2025 Findings.} %MDPI: Please confirm if it is extra contents can be removed.
%  {\em \hl{Rn} %MDPI: We added journal source, volume and page number, please check and confirm.
% } \textbf{2025}, {\em 100}, R2.
%   Available online: \url{http://arxiv.org/abs/2509.04876} (last accessed in May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”.

% \bibitem[Zhang et~al.(2025)Zhang, Fan, Cai, Sun, and Wang]{zhang2025osc}
% Zhang, J.; Fan, Y.; Cai, K.; Sun, X.; Wang, K.
% \newblock {OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in
%   Multi-Agent {LLM} Collaboration}.
% \newblock In \emph{Findings of the Association for Computational Linguistics:
%   EMNLP 2025}; Association for Computational Linguistics: Suzhou, China, 2025;
%   pp. 6320--6337.
% \newblock \url{https://doi.org/10.18653/v1/2025.findings-emnlp.335}.

\bibitem[Zhang et~al.(2025)Zhang, Fan, Cai, Sun, and Wang]{zhang2025osc}
Zhang, J.; Fan, Y.; Cai, K.; Sun, X.; Wang, K.
\newblock {OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent {LLM} Collaboration}.
\newblock In \emph{Findings of the Association for Computational Linguistics: EMNLP 2025};
Association for Computational Linguistics: Suzhou, China, 2025; pp. 6320--6337.
\newblock \url{https://doi.org/10.18653/v1/2025.findings-emnlp.335}.
\newblock Available online: \url{http://arxiv.org/abs/2509.04876} (accessed on 28 May 2026).

\bibitem[Gao et~al.(2024)Gao, Xiong, Gao, Jia, Pan, Bi, Dai, Sun, Guo, Wang,
  and Wang]{gao2024rag}
\hl{Gao, Y.;} %MDPI: Refs. 22 and 39 are duplicated. Please remove duplicated references and rearrange all the references so that they appear in numerical order. Please ensure that there are no duplicated references. RESPONSE: done --- duplicate (rag_survey) removed; this is the retained canonical entry and all \cite calls resolve here (see dedup audit note at end of bibliography).
 Xiong, Y.; Gao, X.; Jia, K.; Pan, J.; Bi, Y.; Dai, Y.; Sun, J.; Guo,
  Q.; Wang, M.;  et~al.
\newblock {Retrieval-Augmented Generation for Large Language Models: A Survey}.
   2024.  Available online: \url{http://arxiv.org/abs/2312.10997} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

\bibitem[Holtzman et~al.(2020)Holtzman, Buys, Du, Forbes, and
  Choi]{holtzman_neural_text_degeneration_2019}
Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; Choi, Y.
\newblock {The Curious Case of Neural Text Degeneration}. {\em \hl{arXiv} %MDPI: We added journal source, arXiv number, please check and confirm. RESPONSE: confirmed.
}
\newblock  \textbf{2020},  arXiv:1904.09751. Available online: \url{http://arxiv.org/abs/1904.09751} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

\bibitem[Wiher et~al.(2022)Wiher, Meister, and
  Cotterell]{wiher_decoding_strategies_2022}
\hl{Wiher, G.;} %MDPI: Refs. 24 and 31 are duplicated. Please remove duplicated references and rearrange all the references so that they appear in numerical order. Please ensure that there are no duplicated references. RESPONSE: done --- duplicate removed; this is the retained canonical entry and all \cite calls resolve here (see dedup audit note at end of bibliography).
 Meister, C.; Cotterell, R.
\newblock {On Decoding Strategies for Neural Text Generators}.
\newblock {\em Trans. Assoc. Comput. Linguist.}
  {\bf 2022}, {\em 10},~997--1012.
\newblock {\url{https://doi.org/10.1162/tacl_a_00502}}.

\bibitem[Ravfogel et~al.(2023)Ravfogel, Goldberg, and
  Goldberger]{ravfogel_conformal_nucleus_sampling_2023}
Ravfogel, S.; Goldberg, Y.; Goldberger, J.
\newblock {Conformal Nucleus Sampling}.
\newblock In {\em Proceedings of the Findings of the Association for Computational
  Linguistics: ACL 2023}; Association for Computational Linguistics: Toronto, ON, Canada, 2023; %MDPI: We are sorry but we could not find the required information about this entry. Please add the name of the publisher and their location. RESPONSE: done --- added publisher (Association for Computational Linguistics) and location (Toronto, ON, Canada).
 pp. 27--34.
\newblock {\url{https://doi.org/10.18653/v1/2023.findings-acl.3}}.

\bibitem[Nguyen et~al.(2025)Nguyen, Baker, Neo, Roush, Kirsch, and
  Shwartz-Ziv]{nguyen_minp_sampling_2025}
Nguyen, M.N.; Baker, A.; Neo, C.; Roush, A.; Kirsch, A.; Shwartz-Ziv, R.
\newblock {Turning Up the Heat: Min-p Sampling for Creative and Coherent {LLM}
  Outputs}. In {\em Proceedings of the International Conference on Learning Representations}; ICLR 2025 Oral; \hl{2025. } %MDPI: We are sorry but we could not find the required information about this entry. Please add the name of the publisher and their location. RESPONSE: [SAVED --- needs team input: ICLR proceedings are published on OpenReview (no traditional publisher); ICLR 2025 was held in Singapore --- please confirm the publisher/location convention to use.]
 Available online: \url{http://arxiv.org/abs/2407.01082} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.


\bibitem[Ji et~al.(2023)Ji, Lee, Frieske, Yu, Su, Xu, Ishii, Bang, Madotto, and
  Fung]{ji_hallucination_nlg_2023}
Ji, Z.; Lee, N.; Frieske, R.; Yu, T.; Su, D.; Xu, Y.; Ishii, E.; Bang, Y.J.;
  Madotto, A.; Fung, P.
\newblock {Survey of Hallucination in Natural Language Generation}.
\newblock {\em ACM Comput. Surv.} {\bf 2023}, {\em 55},~1--38.
\newblock {\url{https://doi.org/10.1145/3571730}}.

\bibitem[Wang et~al.(2023)Wang, Wei, Schuurmans, Le, Chi, Narang, Chowdhery,
  and Zhou]{wang_self_consistency_cot_2023}
Wang, X.; Wei, J.; Schuurmans, D.; Le, Q.; Chi, E.; Narang, S.; Chowdhery, A.;
  Zhou, D.
\newblock {Self-Consistency Improves Chain of Thought Reasoning in Language
  Models}. {\em \hl{arXiv} %MDPI: We added journal source, arXiv number, please check and confirm. RESPONSE: confirmed.
}  \textbf{2023},  arXiv:2203.11171. Available online: \url{http://arxiv.org/abs/2203.11171} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

\bibitem[Kadavath et~al.(2022)Kadavath, Conerly, Askell, Henighan, Drain,
  Perez, Schiefer, Hatfield-Dodds, DasSarma, Tran-Johnson,
  et~al.]{kadavath_language_models_know_2022}
\hl{Kadavath, S.;} %MDPI: Refs. 29 and 33 are duplicated. Please remove duplicated references and rearrange all the references so that they appear in numerical order. Please ensure that there are no duplicated references. RESPONSE: done --- duplicate removed; this is the retained canonical entry and all \cite calls resolve here (see dedup audit note at end of bibliography).
 Conerly, T.; Askell, A.; Henighan, T.; Drain, D.; Perez, E.;
  Schiefer, N.; Hatfield-Dodds, Z.; DasSarma, N.; Tran-Johnson, E.;  et~al.
\newblock {Language Models (Mostly) Know What They Know}. {\em \hl{arXiv} %MDPI: We added journal source, arXiv number, please check and confirm. RESPONSE: confirmed.
}  \textbf{2022}, arXiv:2207.05221.
  Available online: \url{http://arxiv.org/abs/2207.05221} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

\bibitem[Lin et~al.(2022)Lin, Hilton, and
  Evans]{lin2022truthfulqameasuringmodelsmimic}
Lin, S.; Hilton, J.; Evans, O.
\newblock {TruthfulQA: Measuring How Models Mimic Human Falsehoods}. In \hl{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 22--27 May  2022,} %MDPI: We added conference information, please check and confirm. RESPONSE: confirmed.
  Available online: \url{http://arxiv.org/abs/2109.07958} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

% [duplicate removed per editorial dedup: wiher2022decodingstrategiesneuraltext was an alias of wiher_decoding_strategies_2022 (Refs 24 & 31 flagged duplicated by MDPI). All \cite{} calls now point to wiher_decoding_strategies_2022.]

\bibitem[Chang et~al.(2023)Chang, Reitter, Aksitov, and
  Sung]{chang2023kldivergenceguidedtemperaturesampling}
Chang, C.C.; Reitter, D.; Aksitov, R.; Sung, Y.H.
\newblock {KL-Divergence Guided Temperature Sampling}.  {\em \hl{arXiv} %MDPI: We added journal source, arXiv number, please check and confirm. RESPONSE: confirmed.
}  \textbf{2023}, arXiv:2306.01286.
  Available online: \url{http://arxiv.org/abs/2306.01286} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

% [duplicate removed per editorial dedup: kadavath_language_models_mostly_know_2022 was an alias of kadavath_language_models_know_2022 (Refs 29 & 33 flagged duplicated by MDPI). All \cite{} calls now point to kadavath_language_models_know_2022.]

\bibitem[Es et~al.(2025)Es, James, Espinosa-Anke, and Schockaert]{es2023ragas}
Es, S.; James, J.; Espinosa-Anke, L.; Schockaert, S.
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  21--22 March 2025.} %MDPI: We added conference name, location and date information, please check and confirm. RESPONSE: confirmed
  Available online: \url{http://arxiv.org/abs/2309.15217} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

%\pagebreak
\bibitem[Cer et~al.(2017)Cer, Diab, Agirre, Lopez-Gazpio, and
  Specia]{Cer2017SemEvalSTS}
Cer, D.; Diab, M.; Agirre, E.; Lopez-Gazpio, I.; Specia, L.
\newblock {SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and
  Crosslingual Focused Evaluation}.
\newblock In Proceedings of the 11th International Workshop
  on Semantic Evaluation (SemEval-2017). Association for Computational
  Linguistics,  \hl{Vancouver, BC, Canada, 3--4 August 2017;} %MDPI: Newly added location and date information, plaese check and confirm. RESPONSE: confirmed.
 pp. 1--14.
\newblock {\url{https://doi.org/10.18653/v1/S17-2001}}.

\bibitem[Reimers and Gurevych(2019)]{ReimersGurevych2019SentenceBERT}
Reimers, N.; Gurevych, I.
\newblock {Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}.
\newblock In Proceedings of the 2019 Conference on Empirical
  Methods in Natural Language Processing and the 9th International Joint
  Conference on Natural Language Processing (EMNLP-IJCNLP),  \hl{Hong Kong, China, 3--7 November 2019.} %MDPI: Newly added location and date information, plaese check and confirm. RESPONSE: confirmed.
  Available online: \url{http://arxiv.org/abs/1908.10084} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

\bibitem[Min et~al.(2023)Min, Krishna, Lyu, Lewis, Yih, Koh, Iyyer,
  Zettlemoyer, and Hajishirzi]{min2023factscore}
Min, S.; Krishna, K.; Lyu, X.; Lewis, M.; Yih, W.T.; Koh, P.W.; Iyyer, M.;
  Zettlemoyer, L.; Hajishirzi, H.
\newblock {FActScore: Fine-grained Atomic Evaluation of Factual Precision in
  Long Form Text Generation}. In \hl{Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore, 6--10 December 2023,} %MDPI: We added conference name, location and date information, please check and confirm. RESPONSE: confirmed.
  Available online: \url{http://arxiv.org/abs/2305.14251} (accessed on 27 May 2026). %MDPI: Please provide the date you accessed the URL in the following format: “URL (accessed on Day Month Year)”. RESPONSE: done.

\bibitem[Shasha and Wilson(2010)]{shasha2010statistics}
Shasha, D.; Wilson, M.
\newblock {\em {Statistics is Easy!}}; Synthesis Lectures on Mathematics and Statistics; Morgan and Claypool: \hl{San Rafael, CA, USA,} %MDPI: We added the location of the publisher. Please confirm. RESPONSE: confirmed.
 2010.

% [duplicate removed per editorial dedup: rag_survey was an alias of gao2024rag (Refs 22 & 39 flagged duplicated by MDPI). All \cite{} calls now point to gao2024rag.]

% [duplicate removed per editorial dedup: rag_orig was an alias of lewis2020retrieval (Refs 1 & 40 flagged duplicated by MDPI). All \cite{} calls now point to lewis2020retrieval.]

\bibitem[Nygard(2018)]{nygard2018releaseit}
Nygard, M.T.
\newblock {\em {Release It! Design and Deploy Production-Ready Software}}, 2
  ed.; Pragmatic Bookshelf: Raleigh, NC, USA, 2018. %MDPI: We are sorry but we could not find the required information about this entry. Please add the location of the publisher (city and country). RESPONSE: done --- added publisher location (Raleigh, NC, USA).


\end{thebibliography}

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% If authors have biography, please use the format below
%\section*{Short Biography of Authors}
%\bio
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%{\textbf{Firstname Lastname} Biography of first author}
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%\bio
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%{\textbf{Firstname Lastname} Biography of second author}

% For the MDPI journals use author-date citation, please follow the formatting guidelines on http://www.mdpi.com/authors/references
% To cite two works by the same author: \citeauthor{ref-journal-1a} (\citeyear{ref-journal-1a}, \citeyear{ref-journal-1b}). This produces: Whittaker (1967, 1975)
% To cite two works by the same author with specific pages: \citeauthor{ref-journal-3a} (\citeyear{ref-journal-3a}, p. 328; \citeyear{ref-journal-3b}, p.475). This produces: Wong (1999, p. 328; 2000, p. 475)

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