The problem this solves

A language model knows what it was trained on. It has never seen your company handbook, your product catalogue, your policies or last month's minutes. Ask it about any of those and it produces a plausible answer about a generic company, delivered with the same confidence as everything else.

Retrieval augmented generation fixes this by finding the relevant passages from your own documents first, and giving them to the model along with the question. The model then answers from material in front of it rather than from memory.

Why this is the right approach for most business cases. Your documents change, and a system that reads them at question time is always current. It can cite where the answer came from, which is what makes it trustworthy. It works with a few documents or a hundred thousand. And it requires no training of any model.

What it is good for. An internal assistant answering staff questions from policies. A support bot answering from your product documentation. Searching contracts, research, or years of minutes. Anything where the answer exists in writing and nobody can find it.

What it does not fix. If the answer is not in your documents, retrieval finds nothing useful and the model may still invent something. Instructing it to say it does not know is part of the design rather than an optional extra.

And it is not a replacement for organising your documents. A retrieval system over contradictory, out of date material produces confident answers from the wrong version.