Fine-tuning trains a custom version of a model on your own examples — improving quality, consistency, and efficiency for specific tasks.

When Fine-Tuning Makes Sense

• You need a very specific output format or style
• The base model consistently fails at your task even with good prompting
• You want to reduce prompt length (and cost) by baking instructions into the model

Fine-Tuning Process

1. Prepare training data as JSONL (minimum 10 examples, ideally 50–100+)
2. Upload dataset via API
3. Create fine-tuning job
4. Use your custom model ID in API calls

Reference:

Fine-tuning documentation

https://en.wikipedia.org/wiki/Special:Search?search=Fine