RAG vs fine-tuning: choosing the right approach

When teams want a language model to know about their business, they usually reach for one of two approaches: retrieval-augmented generation (RAG) or fine-tuning. They solve different problems, and choosing the wrong one wastes time and money. Here is how we think about it.

What RAG does well

RAG keeps your knowledge in a searchable store and feeds the most relevant pieces to the model at the moment a question is asked. It shines when your information changes often, when you need answers grounded in specific documents, and when you want citations so users can check the source. Update a document and the system is instantly up to date, with no retraining required.

What fine-tuning does well

Fine-tuning adjusts the model itself so it adopts a particular style, format, or narrow skill. It is the right tool when you need consistent tone, a specialised output structure, or reliable behaviour on a repetitive task. What it is not good at is memorising a large, changing body of facts.

A simple rule of thumb

If the question is what does the model know, use RAG. If the question is how does the model behave, consider fine-tuning. Knowledge that changes belongs in retrieval; behaviour that must stay consistent is a candidate for fine-tuning.

Often the answer is both

In practice, many production systems combine the two: RAG supplies current, cited facts while light fine-tuning or careful prompting shapes the tone and format. The art is in the balance, and in building the data pipeline that keeps retrieval accurate.

Not sure which fits your case? See how we approach AI integration or get in touch.