What is Fine-Tuning?
Fine-Tuning — The process of adjusting a pre-trained AI model on a specific dataset to improve its performance on targeted tasks.
Fine-tuning takes a pre-trained model and continues training it on your specific dataset. This teaches the model your domain terminology, writing style, or specialized knowledge. It is most valuable when you need consistent formatting, domain-specific jargon, or behavior that prompt engineering alone cannot achieve.
Frequently Asked Questions
When should I fine-tune vs. use prompt engineering?
Start with prompt engineering. Fine-tune only when you need consistent style, domain-specific terminology, or structured output formats that prompting cannot reliably achieve.
How much data do I need for fine-tuning?
Quality matters more than quantity. As few as 50-100 high-quality examples can produce meaningful improvements for specific tasks. Larger datasets of 1,000+ examples yield more robust results.
Is fine-tuning expensive?
Costs depend on model size and dataset. Fine-tuning a small model can cost under $10. Fine-tuning a large model on a big dataset can cost hundreds to thousands of dollars per training run.