Evidence-Grounded Agentic RAG Architecture and Acceptance Plan

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Agentic RAG Workflow Design for Knowledge-Based AI Systems

Evidence-Grounded Agentic RAG Architecture and Acceptance Plan

Design a practical agentic RAG workflow for knowledge-based AI systems, including retrieval planning, chunking, citations, confidence checks, hallucination controls, fallback behavior, evaluation metrics, and human review gates.

Design an implementation-ready RAG architecture with governed retrieval, citations, tool boundaries, safety controls, evaluation tests, rollout gates, and evidence-based acceptance criteria.

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Useful for deciding whether agentic RAG is justified and producing a traceable architecture, evaluation plan, and controlled implementation roadmap for a knowledge-based AI assistant.

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Citation Readiness

Agentic RAG architecture selection
Knowledge-base retrieval and indexing design
Citation and claim-grounding specification
RAG security and authority-boundary review
RAG evaluation and acceptance planning
Controlled RAG implementation and rollout planning

Project context
AI assistant or system purpose
Target users
Knowledge sources
Relevant files or documents
Preferred AI model or tool stack
Citation requirements
Risk level or compliance concerns
Human review requirements
Definition of done

Project context
Assistant purpose and target users
Knowledge source inventory
Risk and compliance constraints
Definition of done
Representative source samples
Query and answer examples
Tool and system constraints
Citation and provenance requirements
Human review and authority rules
Nonfunctional requirements
Evaluation evidence

Replace each bracketed placeholder with details about your AI assistant, knowledge base, users, tools, and review requirements. Include the types of documents the system will use, such as help articles, PDFs, policies, internal notes, database records, or customer support content. Run the prompt to generate a complete agentic RAG workflow that can guide product planning, technical implementation, and quality testing.

Open ChatGPT and replace every bracketed variable with project-specific information. Provide the relevant evidence and source materials, including a knowledge-source inventory, representative documents, schemas, policies, sample queries, architecture constraints, risk requirements, and any existing evaluation results. Upload or paste only data authorized for ChatGPT, then run the prompt. Review the resulting design, provisional assumptions, test plan, and approval gates with the responsible engineering, security, privacy, compliance, and product owners before implementation.

A SaaS company wants to build a customer support assistant that answers questions using help docs, internal policies, and account data. The team uses this prompt to design an agentic RAG workflow with source selection, chunk ranking, citation checks, fallback behavior, and human review for sensitive billing or compliance-related answers.

A SaaS company is planning a support assistant that must answer from public help articles, versioned internal policies, and authorized account data. The team supplies representative documents, API schemas, sample questions, tenant-isolation rules, citation requirements, and billing escalation policies. ChatGPT produces a proposed architecture decision record, corpus and retrieval specifications, tool-authority register, claim-level citation protocol, fallback matrix, and acceptance tests. Tests remain marked unverified until the team executes them and attaches evidence.

Expert

Expert

ChatGPT

ChatGPT

analysis

analysis

ai-agents
agentic-rag
knowledge-base
citations
hallucination-control
workflow-design
ai-automation
human-review
rag workflow
retrieval augmented generation
confidence scoring
ai architecture

agentic-rag
retrieval augmented generation
rag-architecture
knowledge-governance
citation-verification
retrieval-evaluation
hallucination-control
ai-security
human-review
acceptance-testing

Agentic RAG Workflow Design for Knowledge-Based AI Systems

Agentic RAG Architecture and Acceptance Plan

Design a practical agentic RAG workflow with retrieval planning, chunking, citations, confidence checks, hallucination controls, fallback behavior, evaluation metrics, and human review gates.

Design a governed RAG architecture with retrieval, citations, tool boundaries, safety controls, evaluation tests, rollout gates, and evidence-based acceptance.

Prompt-body line comparison

Removed Added Unchanged context

−You are an expert AI system architect specializing in knowledge-based AI assistants, agentic workflows, and retrieval-augmented generation systems.
+Develop an evidence-grounded RAG architecture and acceptance plan for the system described below. Treat agentic RAG as an option to justify, not a predetermined answer.
 
−Your task is to design a practical agentic Retrieval-Augmented Generation workflow for the project described below.
+Project inputs
 
−Context:
+Blocking inputs for a reliable recommendation:
+- Project context: [Project context]
+- Assistant purpose and target users: [Assistant purpose and target users]
+- Knowledge source inventory: [Knowledge source inventory]
+- Risk and compliance constraints: [Risk and compliance constraints]
+- Definition of done: [Definition of done]
 
−* Project context: [Project context]
−* AI assistant or system purpose: [AI assistant or system purpose]
−* Target users: [Target users]
−* Knowledge sources: [Knowledge sources]
−* Relevant files or documents: [Relevant files or documents]
−* Preferred AI model or tool stack: [Preferred AI model or tool stack]
−* Citation requirements: [Citation requirements]
−* Risk level or compliance concerns: [Risk level or compliance concerns]
−* Human review requirements: [Human review requirements]
−* Definition of done: [Definition of done]
+Useful supporting inputs:
+- Representative source samples: [Representative source samples]
+- Query and answer examples: [Query and answer examples]
+- Tool and system constraints: [Tool and system constraints]
+- Citation and provenance requirements: [Citation and provenance requirements]
+- Human review and authority rules: [Human review and authority rules]
+- Nonfunctional requirements: [Nonfunctional requirements]
+- Evaluation evidence: [Evaluation evidence]
 
−Instructions:
+Input and evidence discipline
 
−1. Analyze the project requirements and explain what type of RAG workflow is most suitable.
−2. Compare basic RAG, enhanced RAG, and agentic RAG for this use case. Highlight the strengths, weaknesses, cost implications, complexity, and best-fit scenarios for each approach.
−3. Design the agentic RAG workflow as a clear step-by-step system flow, including:
+1. Begin with an input sufficiency check. Identify missing, ambiguous, stale, or conflicting information and separate blocking gaps from non-blocking gaps.
+2. Ask concise clarification questions for gaps that could materially change source authorization, privacy controls, architecture selection, escalation rules, or acceptance criteria. If an answer is unavailable, continue only with a bounded draft where safe; preserve the item as an unknown and explain its design impact.
+3. Label substantive statements as one of: supplied fact, observed in supplied material, assumption, recommendation, unresolved conflict, unknown, or reported execution evidence.
+4. Do not invent document contents, source coverage, system access, benchmark results, legal requirements, stakeholder approval, or production behavior.
+5. When sources conflict, record the conflict rather than silently choosing one. Recommend a precedence rule using factors such as source owner, authority, jurisdiction, version, effective date, approval state, and freshness.
 
−   * user query intake
−   * query classification
−   * retrieval planning
−   * source selection
−   * document retrieval
−   * chunk ranking
−   * context assembly
−   * answer generation
−   * citation verification
−   * confidence scoring
−   * hallucination checks
−   * fallback handling
−   * human review or escalation
−   * final response delivery
+ChatGPT operating boundary
 
−4. Propose a retrieval strategy that includes:
+Use ChatGPT to inspect text, files, schemas, logs, test results, and diagrams actually supplied in this conversation and to reason about architecture choices. If browsing, code execution, connectors, or other tools are available, use them only when explicitly enabled and report what was actually inspected or run. Cite or identify the resulting evidence.
 
−   * source prioritization
−   * metadata filtering
−   * hybrid search, if useful
−   * semantic search, if useful
−   * keyword search, if useful
−   * reranking
−   * freshness checks
−   * source quality checks
+Do not imply that ChatGPT accessed an internal repository, vector database, production environment, identity system, ticketing system, or monitoring platform unless corresponding access and results are present in the conversation. Do not modify data, create indexes, call production APIs, deploy components, approve the design, or contact users. Produce a design and test plan only. Any implementation, destructive index operation, permission change, production test, deployment, or approval requires an authorized human and the organization’s change controls.
 
−5. Recommend a chunking strategy, including:
+Architecture analysis
 
−   * chunk size
−   * overlap
−   * metadata fields
−   * document hierarchy
−   * handling tables, FAQs, PDFs, policy documents, and long-form content
+1. Define the assistant’s answerable scope, excluded scope, user groups, trust boundaries, data classifications, and likely query classes. Identify whether answers require document retrieval, structured account data, deterministic computation, transactional tools, or human judgment.
+2. Compare basic RAG, enhanced RAG, and agentic RAG using a decision matrix. Evaluate retrieval complexity, multi-step reasoning need, tool-use need, predictability, attack surface, latency, cost, observability, maintainability, evaluation burden, and failure containment.
+3. Recommend the least complex approach that satisfies the supplied requirements. If agentic behavior is justified, specify exactly which decisions require an agent, which remain deterministic, and the limits on iterations, retrieval calls, tool calls, time, and cost. If it is not justified, recommend basic or enhanced RAG without forcing an agentic design.
+4. Separate the offline knowledge pipeline from the online answer pipeline.
 
−6. Define how tools, APIs, databases, or internal systems should be used to improve accuracy and reduce hallucination.
+Offline knowledge pipeline
 
−7. Establish a citation protocol that explains:
+Design a governed flow for source registration, authorization, extraction, normalization, deduplication, structure preservation, chunking, metadata enrichment, embedding, lexical indexing, validation, publication, monitoring, re-indexing, and retirement.
 
−   * when citations are required
−   * how citations should be selected
−   * how unsupported claims should be handled
−   * how conflicting sources should be handled
−   * how missing information should be reported
+For each source class, address:
+- owner, authority, permitted audiences, tenant or account boundary, jurisdiction, effective date, expiry, version, freshness target, and deletion obligations;
+- parsing and structure preservation for headings, tables, lists, FAQs, PDFs, scanned documents, policy clauses, attachments, and long-form material;
+- chunk boundaries based on semantic units and document hierarchy rather than arbitrary fixed lengths alone;
+- candidate chunk-size and overlap ranges as hypotheses to evaluate, not universal facts;
+- parent-child retrieval, table serialization, adjacent-context expansion, and links back to the original passage;
+- stable document, version, section, and chunk identifiers needed for citations and deletion propagation;
+- access-control metadata enforced before or during retrieval, not merely filtered after generation;
+- quarantine and recovery behavior for malformed, untrusted, stale, unauthorized, or partially indexed content;
+- versioned index releases, validation gates, rollback to the last accepted index, and proof that deletions propagate to lexical, vector, cache, and derived stores.
 
−8. Design confidence and hallucination controls, including:
+Online answer pipeline
 
−   * answerability checks
−   * source support checks
−   * contradiction checks
−   * uncertainty handling
−   * refusal or fallback rules
+Provide a numbered text diagram and a stage contract covering:
+- request authentication and tenant resolution;
+- input validation, prompt-injection screening, and sensitive-data handling;
+- query classification and answerability screening;
+- deterministic policy routing;
+- query rewriting or decomposition when justified;
+- retrieval planning and source selection;
+- metadata and access-control filtering;
+- lexical, semantic, structured-data, or hybrid retrieval;
+- fusion, deduplication, reranking, and context-budget allocation;
+- freshness, authority, contradiction, and coverage checks;
+- context assembly with provenance retained;
+- grounded answer generation;
+- claim extraction and claim-to-source support checking;
+- citation validation;
+- calibrated decision signals for answer, clarify, abstain, or escalate;
+- response policy enforcement and delivery;
+- privacy-aware logging, feedback capture, and monitoring.
 
−9. Specify fallback behaviors for cases where:
+For every stage, state its purpose, inputs, outputs, decision rule, permitted tools, evidence retained, timeout or budget, principal failure modes, fallback path, and any human gate. Keep retrieval, generation, verification, and response authorization distinct.
 
−   * no relevant documents are found
−   * retrieved sources conflict
−   * the user asks for unsupported information
−   * confidence is low
−   * the request is sensitive or high-risk
−   * tool or retrieval systems fail
+Retrieval and grounding design
 
−10. Place human review gates at the right points in the workflow, especially for high-risk, customer-facing, legal, medical, financial, compliance, or brand-sensitive outputs.
+1. Define source-priority rules without relying on a single source. Explain how authority, access rights, effective dates, freshness, query relevance, and source quality interact.
+2. Recommend lexical, semantic, hybrid, metadata-filtered, graph, or structured retrieval only where justified. Specify fusion and reranking behavior, candidate-set limits, and how retrieval diversity is preserved.
+3. Address entity ambiguity, acronyms, multilingual queries, temporal questions, near-duplicate passages, superseded policies, empty filters, and questions spanning multiple documents.
+4. For structured or account-specific data, prefer authorized deterministic APIs or database services over embedding volatile records. Define read-only versus state-changing tools, schema validation, least-privilege credentials, timeouts, retries, idempotency where relevant, and safe error handling.
+5. Treat retrieved text and tool output as untrusted input. Prevent documents from overriding system policy, requesting secrets, changing tool permissions, or directing unauthorized actions.
+6. Define context inclusion and exclusion rules. Explain how the system detects insufficient evidence, conflicting evidence, and excessive context dilution.
 
−11. Define evaluation metrics for:
+Citation and claim-verification protocol
 
−   * retrieval precision
−   * retrieval recall
−   * citation accuracy
−   * answer faithfulness
−   * hallucination rate
−   * fallback quality
−   * latency
−   * user satisfaction
−   * human review accuracy
−   * overall system reliability
+Define:
+- which factual claims require citations and which conversational statements do not;
+- the minimum citation unit and required provenance fields;
+- how a citation must resolve to the exact source version and supporting passage;
+- checks for citation existence, access authorization, passage relevance, entailment, freshness, and source authority;
+- claim-level coverage expectations and handling of partially supported sentences;
+- treatment of derived answers that combine multiple sources or deterministic calculations;
+- behavior for inaccessible, missing, stale, superseded, or contradictory evidence;
+- a rule that unsupported claims are removed, qualified, converted into a clarification request, or refused rather than assigned a decorative citation.
 
−12. Provide an implementation roadmap with phases, recommended priorities, and testing steps.
+Confidence, fallback, and human control
 
−Constraints:
−* Design for modularity, scalability, and maintainability.
−* Avoid over-reliance on a single knowledge source.
−* Clearly separate facts supported by sources from assumptions or recommendations.
−* Include fallback paths for low-confidence or unsupported answers.
−* Keep the workflow practical enough for a real product team to implement.
−* Do not invent project details that were not provided.
−* Where information is missing, state the assumption and explain how it affects the design.
+Do not present an uncalibrated model confidence score as a probability of correctness. Define observable decision signals such as retrieval coverage, reranker separation, source authority, claim support, contradiction status, citation validity, tool success, and query-class risk. Explain how thresholds would be calibrated on representative labeled queries.
 
−Output format:
−Provide a structured design document with the following sections:
+Create a fallback and escalation matrix for no results, weak results, conflicting sources, stale sources, unauthorized sources, unsupported requests, ambiguous identity or tenant, tool timeout, malformed tool output, prompt injection, low evidence coverage, and sensitive high-impact requests. For each condition specify detection evidence, safe user response, retry allowance, logging, escalation destination, and prohibited behavior.
 
−1. Executive summary
−2. Recommended RAG approach
−3. Comparison of basic RAG, enhanced RAG, and agentic RAG
−4. Agentic RAG workflow diagram in text form
−5. Retrieval strategy
−6. Chunking and indexing strategy
−7. Tool and API integration plan
−8. Citation protocol
−9. Confidence and hallucination controls
−10. Fallback behavior design
−11. Human review gate placement
−12. Evaluation metrics
−13. Implementation roadmap
−14. Risks and mitigations
−15. Final recommendations
+Place human review gates only where they change risk. At minimum, evaluate gates for legal, medical, financial, compliance, billing, privacy, security, employment, and consequential account actions. State who may review or approve, what evidence they receive, the service-level expectation, and what happens if no reviewer is available. The generated answer must never represent human approval that has not occurred.
 
−Verification steps:
+Safety and operational controls
 
−* Confirm that every workflow stage has a clear purpose.
−* Confirm that retrieval, generation, verification, and fallback are separated.
−* Check that citation rules are practical and enforceable.
−* Confirm that confidence checks reduce unsupported answers.
−* Confirm that human review gates are placed only where they add value.
−* Confirm that the implementation roadmap is realistic.
+Address tenant isolation, document-level authorization, personally identifiable information, secrets, retention, redaction, encryption assumptions, audit logs, regional restrictions, abuse monitoring, rate limits, denial-of-wallet risks, tool-loop limits, cache leakage, poisoning of the knowledge corpus, data deletion, incident containment, and rollback. Identify stop conditions that should block launch or suspend responses.
 
−Return the complete structured design document.
+Evaluation and acceptance design
+
+Define representative evaluation sets segmented by query class, source class, user authorization, language if applicable, freshness, ambiguity, contradiction, answerable versus unanswerable requests, injection attempts, and risk tier. Prevent test-set leakage and distinguish offline evaluation, adversarial testing, shadow testing, limited rollout, and production monitoring.
+
+Include formulas or precise definitions where applicable for:
+- retrieval precision at k, recall at k, mean reciprocal rank or normalized discounted cumulative gain;
+- authorized-retrieval rate and cross-tenant leakage rate;
+- claim-level citation coverage, citation correctness, and source-version resolution;
+- groundedness or faithfulness and contradiction rate;
+- answerability classification, abstention precision and recall, and escalation accuracy;
+- task success, reviewer agreement, and material-error severity;
+- p50 and p95 latency, timeout rate, tool failure rate, and cost per resolved query;
+- freshness compliance, index failure rate, deletion-propagation compliance, and rollback success.
+
+For every proposed acceptance test, provide: test ID, requirement or risk, test fixture, procedure, expected observation, actual observation, evidence reference, threshold, status, owner, and remediation. Use not run for actual observation and unverified for status unless supplied evidence shows that the test was executed. If evaluation evidence is supplied, distinguish reported results from results directly inspected or reproduced in the available tool context.
+
+Do not invent universal thresholds. Derive thresholds from supplied service requirements, risk tolerance, baseline performance, and labeled evaluation data. Where these are missing, provide clearly labeled provisional targets and the calibration work required before approval.
+
+Required deliverable
+
+Return one structured architecture and acceptance document containing:
+
+1. Input Sufficiency and Evidence Ledger
+A table of each required input, availability, evidence location, reliability, conflicts, assumptions, blocking status, and clarification needed.
+
+2. Scope, Trust Boundaries, and Answerability Contract
+Supported users and query classes, excluded uses, data classifications, system boundaries, and answer, clarify, abstain, or escalate rules.
+
+3. Architecture Decision Record
+A scored comparison of basic, enhanced, and agentic RAG; decisive trade-offs; recommended pattern; rejected alternatives; assumptions; and conditions that would reverse the decision.
+
+4. Component and Data-Flow Design
+A text architecture diagram plus component contracts showing data stores, models, retrievers, rerankers, policy services, tools, verification services, logs, and human-review interfaces.
+
+5. Offline Corpus Governance and Indexing Specification
+Source registry, parsing and chunking rules by source type, metadata schema, authorization enforcement, versioning, freshness, index validation, deletion propagation, release, and rollback.
+
+6. Online Workflow Stage Contract
+A stage-by-stage table with purpose, inputs, outputs, decisions, tools, evidence, budgets, failure paths, and human gates.
+
+7. Retrieval and Context Assembly Specification
+Query routing, candidate generation, filters, fusion, reranking, context allocation, contradiction handling, and representative tuning hypotheses.
+
+8. Tool and Authority Register
+For each API, database, connector, or service: purpose, data accessed, read or write authority, authentication, schema constraints, side effects, timeout, retry policy, audit evidence, failure behavior, and required human authorization.
+
+9. Citation and Claim-Support Specification
+Citation schema, claim-support rules, validation sequence, conflict handling, unsupported-claim behavior, and examples grounded only in supplied materials.
+
+10. Decision-Signal, Fallback, and Escalation Matrix
+Signals, calibration requirements, thresholds or provisional targets, response behavior, retry limits, escalation owners, and prohibited actions.
+
+11. Security, Privacy, and Abuse-Control Register
+Threat or hazard, affected boundary, likelihood and impact rationale, preventive control, detective control, response, residual risk, owner, and launch-blocking status.
+
+12. Evaluation and Acceptance Matrix
+Metric definitions, dataset slices, test cases, expected observations, actual observations, evidence references, thresholds, statuses, and remediation owners.
+
+13. Phased Implementation and Rollout Plan
+Discovery, prototype, offline evaluation, security review, shadow mode, limited rollout, production readiness, monitoring, rollback triggers, dependencies, approval gates, and exit criteria. Keep all phases marked proposed unless execution evidence is supplied.
+
+14. Open Decisions and Handoff Register
+Decision, options, recommendation, accountable owner, required evidence, deadline if supplied, dependency, and current state.
+
+15. Final Recommendation and Readiness State
+Summarize the recommended architecture, highest residual risks, unresolved blockers, next authorized action, and one readiness state: design draft, awaiting inputs, ready for technical review, ready for controlled implementation, or not recommended. Do not use approved, tested, verified, deployed, production-ready, or completed unless the conversation contains matching evidence and authority.
+
+Final verification before responding
+
+- Reconcile every requirement and material risk to at least one architecture control and one evaluation or review method.
+- Confirm that every online stage has defined inputs, outputs, evidence, failure handling, and authority boundaries.
+- Trace representative answer claims from query through authorized retrieval, source version, supporting passage, verification result, and final citation.
+- Confirm access controls are applied before protected content can enter model context.
+- Confirm unsupported, conflicting, stale, unauthorized, and high-risk cases have testable fallback behavior.
+- Confirm agent loops and tool use have explicit budgets, stop conditions, and audit evidence.
+- Confirm acceptance rows contain expected and actual observations and that unexecuted tests remain marked not run and unverified.
+- Reconcile assumptions, open conflicts, residual risks, and blockers with the final readiness state.
+- Return only the requested design document; do not claim implementation or evaluation work occurred when it did not.