RAG vs fine-tuning: which does your business need?

When businesses want an AI model to “know” their information, two approaches come up: retrieval-augmented generation (RAG) and fine-tuning. They solve different problems.

What is RAG?

Retrieval-augmented generation looks up relevant information from your own documents at the moment a question is asked, then gives it to the language model to write an answer. Think of an assistant who checks your files before replying.

What is fine-tuning?

Fine-tuning adjusts a model’s behaviour by training it further on examples. It is useful for teaching a consistent style, format or specialized task, not for storing facts.

Which should you choose?

For most businesses, start with RAG. It works with information that changes, lets you show the source of each answer, lets you control who sees what, and is much cheaper to build and update. If a policy changes tomorrow, you replace the document and the answers update.

Consider fine-tuning when you need the model to follow a precise format, match a tone reliably, or perform a narrow task at scale after simpler methods have been tried.

Can you use both?

Yes. Some systems use RAG for facts and a fine-tuned model for style. It is rarely where to begin.

What matters more than either?

Quality of the source material and testing. A RAG system is only as good as the documents behind it, and every system needs an evaluation set of real questions so you can tell whether changes improve it. Plan for both from the start.

If you are deciding between approaches, book a free consultation and we will help you pick the simplest one that works.