RAG stands for retrieval augmented generation. Before it answers, the AI retrieves relevant material and uses it to write a better response, so it isn’t relying only on what the large language model learned during training. It’s looking up source material at the moment of work.
Think of 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. That’s RAG: giving the model the right reference material before it writes.
How it shows up
It’s easy to call this “we trained the AI on our company data.” We’ve used that phrase, and the practical idea is right: you can give an agent historical bids and ask it to grade a new one. But technically the model’s weights didn’t change. You gave it a way to retrieve context, and that distinction changes what you expect. Put documents in a knowledge base and the AI can use them when they’re retrieved well; if retrieval is bad, the answer misses the key document. Under the hood, documents are broken into pieces, turned into embeddings that help the system find text similar in meaning, often stored in a vector database. When you ask, the system pulls the relevant pieces into context and asks the model to answer from that.
Why you care
RAG is useful when the answer should come from your material, not the internet or the model’s general memory: policies, proposals, transcripts, SOPs, client histories. The point isn’t to make the model “know your business” in some vague way. It’s to put the relevant business record in front of it at the right time, because most useful business answers should be grounded in your actual history, not the model’s best guess.