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. Related: Phase 5 · 23 (Chunking Strategies for RAG) for the six chunking algorithms and when each wins. Phase 5 · 22 (Embedding Models Deep Dive) for picking the embedder. Phase 11 · 07 (Advanced RAG) for hybrid search, reranking, and query transformation. 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). You build a chatbot for your company. A customer asks "What's the refund policy for enterprise plans?" The LLM responds with a generic answer about typical SaaS refund policies. The actual policy, buried in a 200-page internal wiki, says enterprise customers get a 60-day window with pro-rated refunds. The LLM has never seen this document. It cannot know what it was not trained on. Fine-tuning is one…
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…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
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