RAG stands for retrieval augmented generation: before answering, the AI retrieves relevant material and writes from it, instead of relying only on what the large language model learned during training. It looks up your source material at the moment of work.
Picture an open-book analyst. Asked to grade a proposal from memory, they might do a decent job. Let them pull the past winning proposals, the pricing rules, and the old review comments first, and the answer gets much more grounded.
How it shows up
It’s tempting to call this “we trained the AI on our company data.” The practical idea is right, but the model’s weights (its built-in training) never changed: you gave it a way to retrieve context. Under the hood, documents in a knowledge base get broken into pieces, turned into embeddings, and pulled into context when they match your question. If retrieval misses the key document, the answer misses it too.
Why you care
RAG fits whenever the answer should come from your material: policies, proposals, transcripts, SOPs, client histories. The point is putting the relevant business record in front of the model at the right time, so answers stay grounded in your actual history rather than the model’s best guess.