RAG addresses the fact that a model's knowledge is fixed at training time and general. Retrieving the right passages from your own documents and passing them in with the question means the answer can be current and specific to your business.
It reduces invention but does not eliminate it. If retrieval returns nothing relevant, a model will often still answer, so the quality of the retrieval step matters as much as the model, and knowing when to say nothing was found is part of the design.
The practical benefit for a business is citation. Because the answer is grounded in retrieved passages, the system can show which document it came from, which is what makes the output checkable rather than merely plausible.
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Frequently asked
Does RAG stop AI from making things up?
It reduces it substantially by grounding answers in real passages, but it does not remove it. A model handed irrelevant documents may still produce a confident answer, so retrieval quality and a willingness to return nothing are both part of the solution.
Is RAG better than fine-tuning?
They solve different problems. RAG supplies facts the model does not hold and keeps them current. Fine-tuning changes behaviour and style. Needing up-to-date company knowledge is a RAG problem; needing a consistent voice is a fine-tuning one.
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