RAG or fine-tuning? How to choose correctly

Two different problems

RAG and fine-tuning solve different problems, though they are often confused. RAG brings knowledge to the model: it retrieves the right documents and gives them to the model as a basis for the answer. Fine-tuning changes the model’s behaviour: it teaches the model to respond in a certain style, format or tone.

When RAG

Choose RAG when information changes often or there is a lot of it. If answers must be based on current documents, contracts or product information, RAG is almost always the right choice. It also enables citations, which is essential when answers must be traceable.

Advantages of RAG

Knowledge stays current without retraining, answers are traceable and data stays under your control. RAG is also often cheaper and faster to deploy than fine-tuning.

When fine-tuning

Choose fine-tuning when you want to change how the model responds, not what it knows. If you need a consistent style, a specific response format or specialised domain language, fine-tuning can help. It does, however, require high-quality training data and more effort.

Often the best is a combination

In practice, many of the best solutions combine both: a fine-tuned model that answers in the right format, paired with RAG that brings current knowledge. Always start with RAG and good prompt engineering, though — fine-tuning is worth considering only once it adds clear value.

The most common mistakes

The most common mistake is choosing fine-tuning for a knowledge problem. If the model does not know something, fine-tuning usually does not help — it teaches style, not facts. Another typical mistake is building RAG on top of poor data: if the documents are messy or outdated, so are the answers. A third mistake is forgetting evaluation; without a test set you do not know whether a change improved or degraded the solution.

Cost and maintenance

The costs of RAG centre on retrieval infrastructure and document maintenance, whereas the costs of fine-tuning arise from preparing training data and training runs. Over the long term RAG is often more flexible: when information changes, you update the documents rather than the whole model. A fine-tuned model, by contrast, requires a new training round whenever its behaviour needs to change substantially.