AI
Fine-tuning
Training a pre-trained model further on specific data to improve performance for particular tasks.
Why it matters
- Adapts general models to specific domains
- Can improve quality and reduce prompt complexity
- Creates specialized models for your use case
When to use
- When prompting alone does not achieve needed quality
- For domain-specific terminology or style
- When you have quality training data available
Common mistakes
- Fine-tuning before trying good prompting
- Using low-quality or insufficient training data
- Not evaluating against baseline models
Related terms
Need help implementing?
Ready to build with Fine-tuning?
Let us help you implement this in your project.