Fine-Tuning vs Prompting: When to Actually Train Your Own Model

Fine-tuning sounds impressive—your own custom AI model! But after helping dozens of teams evaluate this decision, I can tell you: most don't need it. Understanding when you do is crucial.

Prompting should be your default. It's fast to iterate, requires no training data, and works with the latest model capabilities. Chain-of-thought prompting, few-shot examples, and system prompts solve most customization needs. If you haven't exhausted prompting techniques, you're not ready for fine-tuning.

Fine-tune when you need: consistent formatting that prompts can't reliably produce, domain-specific language the base model handles poorly, behavior modifications that fight the model's defaults, or significant cost reduction through a smaller fine-tuned model. Even then, start with a small fine-tune on a cheap model to validate the approach before committing resources.