How it works in a data room
Documents are split into passages and indexed, often with semantic search so the system can match meaning rather than exact words. When a user asks a question, the system retrieves the most relevant passages from files that user can see, hands them to a language model with the question and asks it to answer only from that material. A good implementation shows citations linking each part of the answer to a page, so the reader can check it in seconds.
Why it matters in a deal
RAG is what turns a general chatbot into a tool that can answer questions about this specific target. It reduces, but does not remove, the risk of hallucination, because the model works from real text. Its quality depends on retrieval: if the right passage is not found, the answer will be incomplete. Security depends on permission-aware AI, which keeps retrieval inside each user’s rights.
Example
A bidder’s analyst asks, “Which customer contracts allow termination on change of control?” The assistant retrieves clauses from 14 contracts, lists them with citations and notes two contracts where the wording is ambiguous. The analyst opens each cited page, confirms 12, and passes the two ambiguous ones to counsel.