RAG connects live enterprise data to a language model at answer time, so responses can be grounded in current documents and systems rather than only what the model memorized in training. It is useful when organizations want assistants that can point to sources, not just sound fluent.
Whilst RAG sounds like “chat over your data,” in practice most failures sit in retrieval and context packing, not in the model. For example, the right PDF is in the corpus but the wrong chunk reaches the prompt, so the answer cites a neighbor section. We often recommend shipping RAG as a measured pipeline — ingest, chunk, retrieve, optionally re-rank, generate — with faithfulness checks, not a single demo prompt.
RAG retrieves relevant passages at query time and injects them into the LLM context before generation. That separates what the model memorized from what your systems can prove today. Failure modes are usually retrieval (wrong chunk, weak hybrid, no re-rank) or context packing (noise drowning the claim), not the LLM alone. Ship with source attribution in the prompt, access control on retrieved docs, and evals for faithfulness — not vibes from a single happy-path demo. Add hybrid search, re-ranking, or graph retrieval when keyword, precision, or multi-hop questions keep slipping through.