Finetuning

When a base model is trained on broad internet text, it learns a lot — but it doesn’t know your product, your tone, or how you want it to handle specific situations. Finetuning is the process of continuing to train that model on a curated set of examples to shape it toward a narrower goal. A customer support bot might be finetuned on transcripts of good support conversations; a medical assistant might be finetuned on clinical notes. The result is a model that behaves more consistently within a defined domain. For behavior architects, finetuning is one of the most powerful levers available — but it requires clean data, clear behavioral goals, and careful evaluation to avoid introducing new problems.