RAG Explained: Give AI the Right Business Context
A general-purpose language model does not automatically know your latest product documentation or internal procedures. Retrieval-augmented generation, usually called RAG, combines a search step with text generation so relevant material can be supplied with the question.
From question to grounded answer
A typical system finds relevant passages in an index, sends those passages to a model and asks it to answer using that context. A useful interface also links to the supporting documents so readers can check the evidence.
Good source material comes first
Search quality depends on the material available. Remove obsolete duplicates, preserve document titles and dates, and assign ownership for updates. Access restrictions must apply to retrieval as well as the original document store: an assistant should not reveal a document a user cannot open.
Test retrieval and answers separately
Our suggested evaluation uses a small set of questions with known supporting documents. First check whether search finds the right passages. Then check whether the answer reflects them. Include questions with no supported answer, conflicting documents and deliberately misleading text. Treat retrieved content as information, not as permission to change system behaviour.
Know the limits
RAG does not guarantee factual answers. Poor search results, stale content or incorrect interpretation can still produce mistakes. Ask the system to acknowledge missing evidence and offer an escalation route. For an internal knowledge pilot, reliable citations and honest uncertainty are more useful than confident answers to every question.
Further reading: Microsoft Learn: Retrieval-augmented generation.