Fine-tuning is the process of training a pre-trained model on a smaller, task-specific dataset to adapt its behavior for a particular use case. Rather than training from scratch, fine-tuning adjusts the model’s weights using examples that reflect the desired output style, domain knowledge, or task format.
In development workflows, fine-tuning can customize a model to follow your team’s coding style, understand your internal APIs, or generate code that matches specific architectural patterns. However, fine-tuning requires significant data preparation and compute resources, and the resulting model may lose some general capabilities.
Many teams find that RAG and carefully structured prompts achieve similar customization with less overhead than fine-tuning.