Basic RAG — embed documents, embed the query, find the nearest vectors, inject into the prompt — works surprisingly well as a starting point. But production RAG systems need more: queries that combine exact keywords with semantic meaning, relevance lists that put the best chunks at the top, and evaluation pipelines that catch regressions before they reach users. This lesson covers all three.