Fine-tuning is the process of continuing to train a pre-trained foundation model on a smaller, domain-specific dataset to specialize its behavior. Rather than training from scratch — which costs millions of dollars of compute — fine-tuning adapts an existing model for specific tasks at a fraction of the cost.
Fine-tuning is used to make models follow specific response formats, adopt a particular tone, or develop expertise in a narrow domain such as medical documentation, legal drafting, or technical support. The risk: over-fine-tuning on narrow data can degrade general-purpose capabilities. RAG is often a better alternative when the goal is knowledge grounding rather than style or format specialization.