Fine-tuning
The process of further training a pretrained model on a curated set of task-specific examples to adapt its behavior to a narrower use case.
In more detail
Fine-tuning takes a general pretrained model and continues training it on examples of the specific behavior you want, shifting the model's defaults toward a particular style, format, or task. It can improve consistency on narrow, high-volume tasks where the desired output is well defined.
It is often reached for too early. Prompting and retrieval solve most problems without the cost, data preparation, and maintenance burden of a fine-tune, and they adapt instantly when requirements change. Fine-tuning earns its keep when a task is stable, high-volume, and not well served by prompting alone.
Where this shows up at Ceven
Ceven gets task-specific behavior primarily through prompting, tool access, and retrieval rather than asking customers to fine-tune models. That keeps a workflow easy to change: when the requirement shifts, you adjust the plain-language description and the steps, rather than retraining and redeploying a model.