Knowledge
RAG or fine-tuning — which is better?
For internal knowledge search and factual knowledge, RAG is almost always the better choice: it is cheaper, can be kept current and returns source references. Fine-tuning tends to pay off when a model needs to master a fixed style, a specific format or narrowly defined tasks.
What RAG does
RAG (retrieval-augmented generation) finds the passages in your own documents that match the question and passes them to the language model as context. The answer draws on these sources, which makes it verifiable and current — without retraining the model.
What fine-tuning does
With fine-tuning, a model is trained further on additional examples. This changes its behaviour permanently, but it is more effort to create and to update. New knowledge requires renewed training.
Rule of thumb
- Knowledge that changes, or where a source reference is required → RAG.
- Fixed style, fixed format or a narrowly defined specialist task → fine-tuning may make sense.
- Often the best approach: RAG as the foundation, adding fine-tuning only where it genuinely helps.
FAQ
Frequently asked questions
Is RAG cheaper than fine-tuning?
As a rule yes, especially in ongoing operation: new content is simply indexed, and there is no need to retrain.
Does RAG provide sources?
Yes. Because the answer is based on the documents that were found, the source can be cited and checked.
Can you combine both?
Yes. RAG is often the foundation, and fine-tuning is added in a targeted way where style or format matter.