Fine-Tuning

Purpose

Decide whether fine-tuning is warranted, and do it properly if it is. Most fine-tuning projects should have been prompt engineering or retrieval, and the ones that should be fine-tuning usually fail on dataset quality rather than on the training.

When to Use

• A task where prompting has plateaued below the required accuracy.
• A specific output format, style, or domain vocabulary the model will not adopt reliably.
• Reducing cost by making a small model do what currently requires a large one.
• Evaluating an existing fine-tuned model that underperforms.

Capabilities