Fine-tuning
The process of training an existing AI model on specific data to customize its behavior for particular tasks.
Why Fine-tune?
Base AI models are generalists. They know a lot about everything but aren't specialized for your specific needs.
Fine-tuning teaches a model your:
- Writing style
- Domain terminology
- Specific formats
- Company knowledge
After fine-tuning, the model responds more naturally for your use case without extensive prompting.
Fine-tuning vs Prompting
Prompting: Give instructions each time. "Write in a casual tone. Use these terms. Format like this..."
Fine-tuning: Train the model once. It just knows your preferences automatically.
Fine-tuning is more work upfront but saves effort (and tokens) long-term if you're using the model repeatedly.
When Fine-tuning Makes Sense
- You have consistent, specific requirements
- You're making many similar requests
- Prompting alone isn't getting the quality you need
- You have training data that represents what you want
When Fine-tuning Doesn't Make Sense
- Your needs vary significantly between tasks
- You don't have good training examples
- You're just experimenting
- A better base model might solve your problem
The RAG Alternative
For many use cases, RAG (retrieval-augmented generation) works better than fine-tuning. Instead of changing the model, you feed it relevant documents at query time. This is often easier and more flexible than fine-tuning.