From dbc093ce43c940183e939072008aaebc8db6dc90 Mon Sep 17 00:00:00 2001 From: Narendra Tiwari <61331906+nsavarn@users.noreply.github.com> Date: Thu, 12 Feb 2026 21:07:02 +0530 Subject: [PATCH] Add flagship capstone outline for governed Agentic RAG system --- FLAGSHIP_CAPSTONE_OUTLINE.md | 332 +++++++++++++++++++++++++++++++++++ 1 file changed, 332 insertions(+) create mode 100644 FLAGSHIP_CAPSTONE_OUTLINE.md diff --git a/FLAGSHIP_CAPSTONE_OUTLINE.md b/FLAGSHIP_CAPSTONE_OUTLINE.md new file mode 100644 index 0000000..4c78b2d --- /dev/null +++ b/FLAGSHIP_CAPSTONE_OUTLINE.md @@ -0,0 +1,332 @@ +# Enterprise GenAI Knowledge Copilot — Flagship Capstone Project Outline + +## 1) Executive Summary +**Project title:** Enterprise GenAI Knowledge Copilot (Governed Agentic RAG System) + +Build a production-grade, governance-first Agentic RAG platform for enterprise decision support. The system is designed to provide citation-backed answers, enforce role-based controls, log traceable reasoning steps, and maintain measurable quality through a continuous evaluation loop. + +> This is not a general chatbot. It is a traceable, defensible, and audit-ready AI decision-support system. + +--- + +## 2) Problem Framing +### Business Problem +Most enterprise GenAI deployments fail under real-world governance requirements because they: +- Hallucinate unsupported facts +- Lack end-to-end audit trails +- Do not enforce policy and access constraints +- Have weak or absent evaluation feedback loops +- Cannot withstand regulatory scrutiny + +### Capstone Goal +Deliver a governed AI architecture that is reliable, measurable, and regulation-aware by design. + +--- + +## 3) Project Vision and Outcomes +### Vision +Create a system that can answer enterprise knowledge queries with evidence-grounded responses while operating under explicit governance and risk controls. + +### Target Outcomes +- **Accuracy:** High faithfulness and relevance on enterprise QA tasks +- **Traceability:** Full reasoning and decision log for each response +- **Governance:** Policy, RBAC, and safety constraints enforced at runtime +- **Operational Excellence:** Measured latency, cost, and drift with alerting +- **Regulatory Readiness:** Mapped controls aligned to NIST AI RMF and EU AI Act expectations + +--- + +## 4) Scope Definition +### In Scope +- Text document ingestion and indexing +- Hybrid retrieval (vector + keyword) with re-ranking +- Agentic planner with retrieval/tool decision logic +- Citation-backed response generation +- Automated evaluation pipeline and dashboarding +- Policy enforcement, confidence gating, and escalation logic +- Observability for quality, cost, and latency + +### Out of Scope (for baseline) +- Full production SSO/IdP integration +- Cross-region deployment and HA/DR hardening +- Multimodal retrieval as a mandatory feature (stretch goal) + +--- + +## 5) Logical Architecture +1. **User Interface (Web/API)** +2. **Agent Orchestrator (Planner)** +3. **Hybrid Retrieval Layer** (Vector + Keyword + Re-ranking) +4. **Context Assembly Engine** +5. **LLM Generation Layer** +6. **Evaluation Engine** (Faithfulness, Groundedness, Relevance) +7. **Policy & Governance Layer** +8. **Audit Log & Monitoring** + +### Key Design Principle +Every answer must be explainable, attributable to trusted sources, and reviewable post hoc. + +--- + +## 6) Modular Work Breakdown Structure + +## Module A — Data & Knowledge Pipeline +### Objectives +- Ingest and normalize enterprise documents +- Compare chunking strategies (semantic, recursive, fixed-size) +- Create embedding + metadata enrichment pipeline + +### Deliverables +- Chunking strategy comparison notebook +- Metadata schema (`source`, `authority`, `date`, `access_level`, `doc_type`, `confidence_tag`) +- Embedding versioning strategy and lineage log + +### Success Criteria +- Repeatable ingestion workflow +- Searchable metadata with access tags +- Re-index process with minimal downtime + +--- + +## Module B — Retrieval Engine +### Objectives +- Build hybrid retrieval pipeline +- Add cross-encoder re-ranking stage +- Tune top-k, score thresholds, and fallback behavior + +### Deliverables +- Retrieval benchmark report (precision/recall/MRR) +- Recall vs precision comparison charts +- Retrieval confidence gating logic + +### Success Criteria +- Stable retrieval quality across query classes +- Reduced irrelevant chunks post re-ranking +- Deterministic low-confidence handling + +--- + +## Module C — Agentic Orchestration +### Objectives +- Planner decides if retrieval is needed +- Select best data source/tool by intent and policy +- Enforce controlled autonomy and tool limits + +### Deliverables +- Multi-step reasoning trace examples +- Retrieval-aware agent workflow (LangGraph) +- Policy-constrained tool invocation logic + +### Success Criteria +- Transparent planning traces +- No unauthorized tool/data calls +- Reproducible decision paths + +--- + +## Module D — Evaluation Framework +### Metrics +- Faithfulness +- Groundedness +- Answer relevance +- Citation coverage +- Hallucination rate + +### Deliverables +- Golden evaluation dataset +- LLM-as-a-Judge + programmatic scoring pipeline +- Evaluation dashboard and scorecards by query type + +### Success Criteria +- Automated pre-release quality checks +- Regression alerts for score degradation +- Repeatable benchmark runs per version + +--- + +## Module E — Governance & Controls +### Controls Implemented +- Role-based retrieval filtering +- Output moderation and policy checks +- Confidence-based suppression +- Human-in-the-loop escalation + +### Deliverables +- Governance flow diagram +- Incident classification model +- Kill-switch and safe-fallback logic + +### Success Criteria +- Unauthorized information never surfaced +- Policy violations blocked and logged +- Escalation path works in ambiguous/high-risk cases + +--- + +## Module F — LLMOps & Observability +### Objectives +- Track latency, quality, drift, and cost +- Version prompts and embeddings +- Operationalize deployment and rollback path + +### Deliverables +- Metrics logging module +- Drift detection simulation +- CI/CD blueprint for GenAI releases + +### Success Criteria +- End-to-end telemetry per request +- Prompt/version traceability +- Actionable operational alerts + +--- + +## 7) Recommended Technology Stack +| Layer | Tools | +|---|---| +| Programming | Python (Anaconda) | +| Vector DB | FAISS / Chroma | +| Embeddings | Sentence Transformers | +| LLM | OpenAI API / Llama | +| Re-ranking | Cross-Encoder | +| Agent Framework | LangGraph | +| API Serving | FastAPI | +| Evaluation | RAGAS + custom LLM judge | +| Monitoring | Structured logging + dashboards | + +--- + +## 8) Data Model and Governance Metadata +Minimum metadata per chunk/document: +- `document_id` +- `chunk_id` +- `source_system` +- `source_url_or_path` +- `owner` +- `authority_level` +- `created_at` +- `updated_at` +- `access_level` (RBAC tag) +- `retention_policy` +- `embedding_version` +- `ingestion_hash` + +Purpose: citation traceability, access governance, and reproducibility. + +--- + +## 9) Evaluation KPIs and Targets +| Metric | Target | +|---|---| +| Faithfulness | > 90% | +| Groundedness | 100% citation-backed claims | +| Hallucination Rate | < 5% | +| P95 Latency | < 3 sec | +| Cost per Query | Within defined budget | + +### KPI Gating Policy +- Block promotion if faithfulness falls below threshold +- Block promotion if citation coverage is incomplete +- Trigger incident review on hallucination spike + +--- + +## 10) Governance and Risk Alignment +### Framework Mapping +- **NIST AI RMF:** Govern, Map, Measure, Manage +- **EU AI Act readiness:** documentation, transparency, controls, and auditability for high-risk-like operational expectations + +### Evidence Artifacts for Compliance Review +- Model card and system card +- Data lineage and retention artifacts +- Access-control and audit logs +- Evaluation reports and incident logs + +--- + +## 11) Repository and Deliverable Structure +```text +/data_pipeline +/retrieval_engine +/agent_orchestrator +/evaluation +/governance +/llmops +/docs + architecture.md + evaluation_report.md + governance_model.md +``` + +### Suggested Additional Files +- `/docs/threat_model.md` +- `/docs/decision_log.md` +- `/docs/runbook.md` +- `/configs/policy_rules.yaml` +- `/configs/prompt_registry.yaml` + +--- + +## 12) Implementation Roadmap (12 Weeks) +### Phase 1 (Weeks 1–2): Foundations +- Define use cases and non-functional requirements +- Set up ingestion baseline and schema +- Build initial retriever and API skeleton + +### Phase 2 (Weeks 3–5): Retrieval + Agent Core +- Implement hybrid retrieval + re-ranking +- Add planner graph and tool routing +- Add citation generation and source attribution + +### Phase 3 (Weeks 6–8): Evaluation + Governance +- Build golden dataset and evaluation pipelines +- Add RBAC filters, moderation, confidence gating +- Implement escalation and kill-switch + +### Phase 4 (Weeks 9–10): LLMOps + Observability +- Instrument latency/cost/quality metrics +- Add drift checks and alerting +- Implement prompt + embedding version control + +### Phase 5 (Weeks 11–12): Hardening + Capstone Packaging +- Run red-team and failure-mode tests +- Finalize dashboard and governance report +- Prepare architecture walkthrough and demo + +--- + +## 13) Demo Narrative (Final Presentation) +1. User asks a policy-sensitive enterprise query +2. Planner decides retrieval strategy and authorized data scope +3. Retrieved evidence is re-ranked and assembled +4. LLM response is generated with citations +5. Evaluation scores computed and displayed +6. Policy engine approves/suppresses/escalates output +7. Full reasoning + audit trail is shown in monitoring view + +Outcome: demonstrates technical capability **and** enterprise trustworthiness. + +--- + +## 14) Risks and Mitigation +- **Risk:** Hallucinated synthesis despite retrieval + **Mitigation:** stricter citation coverage checks + confidence gating +- **Risk:** Access-policy leakage + **Mitigation:** pre-retrieval RBAC filtering + post-generation policy scan +- **Risk:** Cost overrun from excessive context/tool calls + **Mitigation:** planner budget constraints + caching + query shaping +- **Risk:** Quality drift over time + **Mitigation:** scheduled benchmark runs + drift alerts + rollback protocol + +--- + +## 15) Optional Stretch Extensions +- Multimodal ingestion (documents + images) +- Model routing (SLM vs LLM) by complexity and risk +- Automated prompt regression suite +- Synthetic data generation for hard evaluation cases + +--- + +## 16) Final Positioning Statement +This capstone represents a transition from NLP fundamentals to enterprise-grade AI architecture. It proves the ability to design and operate a governed Agentic RAG platform that is measurable, auditable, and aligned to modern AI risk frameworks.