A Retrieval-Augmented Generation (RAG) system, at its simplest, retrieves relevant chunks of your own documents and passes them to a language model alongside the user's question. The architecture is easy to sketch: ingest documents, chunk them, embed them, store the embeddings, retrieve at query time, and generate an answer.
In practice, the quality of a RAG system is decided almost entirely by decisions that never appear in that diagram: how you chunk documents (naive fixed-size chunking loses context; structure-aware chunking preserves it), how you handle documents that change over time, and how you evaluate whether retrieved chunks are actually relevant before you ever look at the generated answer.
Two practical considerations we push clients on early: first, what happens when the answer isn't in the documents at all — a good RAG system should be able to say so, rather than generate a fluent but wrong answer. Second, retrieval quality should be evaluated separately from generation quality; a good language model can't fix bad retrieval.
For production systems, we also plan for citation/traceability (showing which source a claim came from), access control (not every user should retrieve every document), and monitoring for when retrieval quality degrades as your document set grows.
RAG is a strong pattern for knowledge-base assistants, internal search, and document Q&A — but it's an engineering system with real failure modes, not a plug-and-play feature.