Fine-tuning

Fine-tuning is further training an existing AI model on your own examples so it behaves in a specific way.

In practice: You start with a general model and train it on hundreds or thousands of examples of the output you want, such as your support replies or your product descriptions. The result follows your style or format more reliably than prompting alone, but it does not reliably learn new facts.

Why it matters when picking a tool

For most businesses, good prompts plus RAG beat fine-tuning: they are cheaper, faster to change and easier to keep current. Fine-tuning pays off when you have many consistent examples and a narrow, repeated task. Be wary of tools that promise a model "trained on your data" without saying how.