RAG (Retrieval-Augmented Generation)
RAG is the technique of searching your own documents for passages relevant to a question, then giving those passages to a language model along with the question, so the answer is grounded in your content rather than the model's training data.
It exists because models don't know your prices, your policies or your product. Retraining one on your documents is expensive and goes stale immediately. RAG sidesteps both: keep the documents in a searchable store, fetch the relevant few at question time, and let the model answer from those.
Almost every RAG failure is a retrieval failure, not a model failure. If the search returns the wrong three paragraphs, no model can rescue the answer. Which means the work is in the unglamorous part — how documents are split, what gets indexed, whether headings and tables survive the conversion — not in prompt wording.
Retrieval usually combines keyword and semantic search. Semantic alone reliably misses exact identifiers: part numbers, error codes, policy references.
Why it matters
RAG is what makes a support chatbot answer from your actual policies instead of inventing them, and it's why a bot built on your documents costs more than one that isn't — the document pipeline is most of the work.
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