Project 6: RAG Bot with Citations

Build a RAG bot that always cites sources and refuses to answer when evidence is missing.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 10-16 hours
Language Python or JavaScript
Prerequisites RAG basics, vector store usage
Key Topics grounding, citation enforcement, refusal logic

1. Learning Objectives

By completing this project, you will:

  1. Enforce citation requirements in outputs.
  2. Implement refusal logic when evidence is missing.
  3. Measure citation coverage and accuracy.
  4. Trace answers back to chunk IDs.
  5. Reduce hallucination rates.

2. Theoretical Foundation

2.1 Evidence-First Responses

Citations are a contract: no citation, no claim. This shifts the system from creative generation to accountable synthesis.

3. Project Specification

3.1 What You Will Build

A RAG bot that answers from a document set and outputs citations for every factual claim.

3.2 Functional Requirements

  1. Retriever with top-k chunks.
  2. Answer prompt requiring citations.
  3. Citation validator that checks chunk IDs.
  4. Refusal mode if evidence is insufficient.
  5. Metrics for citation coverage.

3.3 Non-Functional Requirements

  • Deterministic mode for testing.
  • Transparent outputs with source links.
  • Safe fallback for unknown queries.

4. Solution Architecture

4.1 Components

Component Responsibility
Retriever Fetch evidence chunks
Answer Chain Generate cited response
Validator Ensure citations exist
Refusal Logic Handle insufficient evidence

5. Implementation Guide

5.1 Project Structure

LEARN_LANGCHAIN_PROJECTS/P06-rag-citations/
├── src/
│   ├── retrieve.py
│   ├── answer.py
│   ├── validate.py
│   ├── refuse.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Retriever + answer (4-6h)

  • Build top-k retrieval.
  • Checkpoint: answers include citations.

Phase 2: Validator + refusal (3-5h)

  • Validate citations match chunks.
  • Checkpoint: missing citations trigger refusal.

Phase 3: Metrics (3-5h)

  • Measure citation coverage.
  • Checkpoint: report shows coverage %.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit validation missing citation
Integration pipeline retrieval + answer
Regression refusal unknown queries refuse

6.2 Critical Test Cases

  1. Output without citations is rejected.
  2. Citation points to non-existent chunk triggers failure.
  3. No-evidence query yields refusal.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Fake citations IDs not found validate against index
Weak refusal hallucinates anyway hard enforce refusal
Low coverage too few citations adjust prompt + chunking

8. Extensions & Challenges

Beginner

  • Add citation formatting styles.
  • Add a “sources” section.

Intermediate

  • Add reranking before answering.
  • Add confidence scores per claim.

Advanced

  • Add citation quality scoring.
  • Add multi-source consensus.

9. Real-World Connections

  • Legal and medical apps require strict citations.
  • Enterprise search needs accountable answers.

10. Resources

  • LangChain RAG docs
  • Grounded generation best practices
  • “AI Engineering” (safety and evals)

11. Self-Assessment Checklist

  • I can enforce citations in outputs.
  • I can block unsupported claims.
  • I can measure citation coverage.

12. Submission / Completion Criteria

Minimum Completion:

  • RAG bot with citations
  • Refusal mode for no evidence

Full Completion:

  • Citation validator
  • Coverage report

Excellence:

  • Reranking + confidence scoring
  • Citation quality metrics

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LANGCHAIN_PROJECTS.md.