Fine-Tuning

The process of further training a pre-trained AI model on domain-specific data to specialize its capabilities for particular tasks.

Fine-tuning adapts a pre-trained AI model to perform better on specific tasks or domains by training it further on specialized data.

How It Works

1. Start with a foundation model trained on broad data
2. Prepare domain-specific training examples
3. Continue training with lower learning rates
4. Validate performance on held-out test data

Benefits

• Efficiency: Requires far less data than training from scratch
• Specialization: Better performance on target tasks
• Cost: Cheaper than training large models
• Control: Customize behavior and outputs

Trade-offs

• Can reduce general capabilities (catastrophic forgetting)
• Requires quality training data
• May inherit or amplify base model biases
• Ongoing maintenance as base models update

Alternatives

• Prompt engineering: No training, just better instructions
• RAG: Retrieval-augmented generation for knowledge
• LoRA: Low-rank adaptation for efficient fine-tuning