Different tools for different problems
Retrieval-augmented generation gives a model access to information at the moment it answers. Fine-tuning changes how the model behaves by training it on examples.
If the problem is that the model does not know your facts, start with retrieval. If the problem is that the model does not behave the way you need — format, tone or a specialised task — consider fine-tuning.
When RAG is the right starting point
- Your information changes frequently.
- Users need citations to verify answers.
- Access to information depends on who is asking.
- You want to avoid retraining whenever content changes.
When fine-tuning earns its place
- You need consistent output structure or style at high volume.
- A smaller, cheaper model must match a larger model on a narrow task.
- You have a high-quality set of labelled examples.
Often, the answer is both
Many mature systems use retrieval for knowledge and a tuned model for behaviour. Start simple, evaluate, and add complexity only when the measurements justify it.