AI
What is the difference between fine-tuning and prompt engineering?
Fine-tuning modifies a model's weights by training it on domain-specific data, permanently changing its behavior. Prompt engineering achieves different outputs by crafting better instructions without altering the model itself. Fine-tuning is costlier and slower but produces more consistent results for specialized tasks; prompt engineering is faster to iterate and requires no training infrastructure.
Key Considerations
- Start with prompt engineering — it's free, instant, and often sufficient for 80% of use cases
- Fine-tuning requires hundreds to thousands of high-quality labeled examples to be effective
- Modern approaches like few-shot prompting and system prompts close much of the gap
- Fine-tuning makes sense when you need consistent formatting, domain terminology, or reduced latency from shorter prompts
- Evaluate cost: fine-tuning runs can cost $50–$500+ and must be repeated when the base model updates