Model Fine-Tuning

A practical example

Example: a company with thousands of past winning proposals fine-tunes a model so drafts automatically follow its proposal structure and terminology, instead of pasting style rules into every prompt.

What to evaluate before investing

• Ask how much labeled data the vendor needs and who is responsible for preparing and cleaning it.
• Check where your training data is stored, whether it trains shared models, and how it is deleted on request.
• Ask for a way to compare the fine-tuned model against a well-prompted base model on your own tasks before committing.

Limitations and tradeoffs

Fine-tuning is often unnecessary: a strong base model with good prompts and examples matches or beats it for many tasks, at far lower cost and risk.