Fine-tuning adapts general-purpose models to specific use cases, often improving quality and reducing prompt complexity.
When to Fine-Tune
- Consistent style/format needed
- Domain-specific terminology
- Specific task performance
- Reduced latency requirements
Trade-offs
- Training data required
- Ongoing maintenance
- Potential catastrophic forgetting
- Higher operational complexity