Fine-tuning

Fine-tuning is additional training on top of an existing model, using your own examples, so it permanently adjusts how the model responds.

It's one of two ways to customize an AI's behavior for a specific job: change the model itself (fine-tuning), or change what you feed it at the moment of the request (prompts, tools, reference documents). Fine-tuning is slower and more expensive, but the two approaches solve different problems.

Masterforce leans entirely on the second approach — a Skill, a system prompt, and real workspace context — rather than fine-tuning a model. Combined with bring-your-own-AI, that means switching models is a configuration change, not a retraining project.