AI Engineering/Phase 11/Lesson 06

RAG (Retrieval-Augmented Generation)

Your LLM knows everything up to its training cutoff. It knows nothing about your company's docs, your codebase, or last week's meeting notes. RAG solves this by retrieving relevant documents and stuffing them into the prompt. It's the most deployed pattern in production AI. If you build one thing from this course, build a RAG pipeline.

Build 1.5 hr Python

Prerequisites: Phase 10 (LLMs from Scratch), Phase 11 Lessons 01-05

#Learning Objectives

Build a complete RAG pipeline: document loading, chunking, embedding, vector storage, retrieval, and generation
Implement semantic search using a vector database (ChromaDB, FAISS, or Pinecone) with proper indexing
Explain why RAG is preferred over fine-tuning for knowledge-grounded applications (cost, freshness, attribution)
Evaluate RAG quality using retrieval metrics (precision, recall) and generation metrics (faithfulness, relevance)

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.