In fine-tuning, an already-trained model is trained further on a smaller, task-specific dataset. This way it learns, for example, a brand's tone of voice or the format of specialized reports. Fine-tuning is worthwhile when prompt engineering alone is not enough and consistent behavior across many requests is required. As an alternative, RAG often makes sense, integrating knowledge from external data sources without changing the model.
Fine-Tuning
Fine-tuning is the targeted retraining of a pre-trained AI model with your own data to specialize it for a specific task or style. It permanently adjusts the model's weights.