Most "RAG tutorials" hand you a framework and a .from_documents() one-liner, and you never actually see what happens inside. So I built one by hand — chunking, embeddings, a tiny vector store, hybrid retrieval, re-ranking, and cited generation — to understand each moving part. Here's the mental model and the two pieces that matter most. ## What RAG actually is An LLM only knows what was in its training data. RAG (Retrieval-Augmented Generation) lets it answer questions about your...