Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
/ quick answer
Fine-Tuning adjusts a pretrained model's weights using a curated dataset so it follows a specific style, format, or task more reliably than prompting alone. It is most valuable when you need consistent tone, strict output schemas, or to compress a long system prompt.
What is Fine-Tuning?
Fine-Tuning adjusts a pretrained model's weights using a curated dataset so it follows a specific style, format, or task more reliably than prompting alone. It is most valuable when you need consistent tone, strict output schemas, or to compress a long system prompt.
What is an example of Fine-Tuning?
An e-commerce brand fine-tunes a small model on 5,000 of their best support replies, then runs it for first-line triage — keeping voice and policy consistent without a 3,000-token system prompt.
Why does Fine-Tuning matter for AI and automation?
Continuing to train a base model on your own examples to specialize its behavior. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.