Fine-Tuning

Fine-tuning retrains a model's parameters on a large set of examples so it consistently responds in a specific tone or performs more accurately on a narrow task.

It suits advanced needs where the use case is stable and enough consistently labeled data has accumulated. A common misconception is expecting fine-tuning to compensate for insufficient data — with too little data or a still-shifting use case, its return on investment is usually low, and RAG is typically the more practical starting point.