A small RAG demo is straightforward to build. Load a few files, generate embeddings, store them in a vector database, retrieve similar chunks, and put them into a prompt. A system people rely on is a different problem.

Real applications need reliable parsing, stable chunk IDs, metadata, access control, incremental indexing, retrieval evaluation, failure handling, and observability. If those pieces are weak, the model receives poor evidence and can turn it into a polished but wrong answer.

This chapter builds a practical RAG pipeline: ingestion, chunking, indexing, query processing, context assembly, answer generation, and the operational details that keep the system maintainable.