A model’s training data has a cutoff date and doesn’t know your company’s contracts, product manuals, or last quarter’s numbers. RAG closes that gap: before generating an answer, the system searches a store of your own documents for the most relevant passages and feeds those into the model as context. The model then answers from that retrieved material rather than from memory alone.
This is the standard approach for enterprise AI that needs to be current and specific rather than generic. It’s also the standard defence against a model inventing an answer when it doesn’t actually know one, because a well-built RAG system can decline to answer when nothing relevant was retrieved.
“Governed” RAG is the harder, more important version: access controls on what each user’s queries are allowed to retrieve, citations back to source documents, and monitoring for when retrieval quality degrades. A RAG system without those is a liability with good branding.