Project 3: Build a Complete RAG System (No LangChain)

Build an end-to-end RAG pipeline from scratch: ingestion, chunking, embeddings, retrieval, and grounded answers.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 1–2 weeks
Language Python
Prerequisites Embeddings basics, HTTP APIs
Key Topics chunking, retrieval, grounding, eval

Learning Objectives

By completing this project, you will:

  1. Ingest and chunk documents with consistent boundaries.
  2. Generate embeddings and store them with metadata.
  3. Retrieve top-k context for queries.
  4. Generate grounded answers with citations.
  5. Evaluate retrieval and answer quality.

The Core Question You’re Answering

“How do you build a RAG system that doesn’t rely on frameworks but still produces reliable, grounded answers?”

This project strips away abstractions so you control each step.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Chunking strategies Context quality RAG guides
Embedding similarity Retrieval relevance Vector search basics
Prompt grounding Reduce hallucination LLM prompting guides
Evaluation Verify quality IR metrics

Theoretical Foundation

RAG Pipeline

Docs -> Chunks -> Embeddings -> Vector Index -> Retrieved Context -> Answer

The quality of each stage compounds into final output quality.

Project Specification

What You’ll Build

A CLI or small API that ingests documents and answers questions with citations.

Functional Requirements

  1. Document ingestion + chunking
  2. Embedding generation
  3. Vector index storage
  4. Retrieval top-k for queries
  5. Answer generation with citations

Non-Functional Requirements

  • Deterministic index building
  • Transparent citations
  • Safe fallback for empty retrieval

Real World Outcome

Example query:

$ rag query "What is the refund policy?"

Example response:

Answer: The refund policy allows returns within 30 days. [doc_12]
Sources: doc_12 (Section 3.2)

Architecture Overview

┌──────────────┐   ingest  ┌──────────────┐
│ Document Set │──────────▶│ Chunker      │
└──────────────┘           └──────┬───────┘
                                  ▼
                           ┌──────────────┐
                           │ Embedder     │
                           └──────┬───────┘
                                  ▼
                           ┌──────────────┐
                           │ Vector Index │
                           └──────┬───────┘
                                  ▼
                           ┌──────────────┐
                           │ Answerer     │
                           └──────────────┘

Implementation Guide

Phase 1: Ingestion + Chunking (3–5h)

  • Implement chunker
  • Checkpoint: chunks consistent and labeled

Phase 2: Embeddings + Retrieval (4–8h)

  • Build vector index
  • Checkpoint: top-k retrieval returns relevant chunks

Phase 3: Answering + Evaluation (4–8h)

  • Add citations in response
  • Run evaluation queries
  • Checkpoint: answers grounded in sources

Common Pitfalls & Debugging

Pitfall Symptom Fix
Bad chunking irrelevant answers tune size/overlap
Hallucinations unsupported claims enforce citations
Slow retrieval high latency reduce top-k or use ANN

Interview Questions They’ll Ask

  1. How does chunk size affect retrieval quality?
  2. What should you do when retrieval finds no relevant docs?
  3. Why are citations critical for RAG trust?

Hints in Layers

  • Hint 1: Start with a tiny document set.
  • Hint 2: Add chunk IDs and metadata.
  • Hint 3: Enforce citation format in outputs.
  • Hint 4: Build a small eval set to test quality.

Learning Milestones

  1. Ingested: documents chunked and indexed.
  2. Grounded: answers cite correct sources.
  3. Measured: evaluation shows quality.

Submission / Completion Criteria

Minimum Completion

  • End-to-end RAG pipeline

Full Completion

  • Citations + evaluation set

Excellence

  • Reranking or hybrid retrieval

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