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# 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.