Foundations
Fine-tuning
Further training a model on your own examples to change its behaviour — usually not the answer you are looking for.
Fine-tuning is often the first thing people ask about and rarely the right first step.
It teaches style and format well: a consistent tone, a specific output shape, a domain's phrasing. It teaches facts poorly, and it bakes them in at a point in time — when your data changes you must retrain.
For "the model should know about our products", RAG is almost always better: cheaper, updatable instantly, and able to cite sources.
Consider fine-tuning once retrieval and prompting are genuinely exhausted, and you have the labelled examples to do it with.