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.
Reviewed by the Mana Studio team · Last reviewed
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.
We build this. AI Chatbots starts at $299 — every price is published.
Related terms
More in AI Automation & Agents
Need this built rather than explained?
We publish every price we charge, and you get a quote in writing before anything starts.