Best practices for fine-tuning GPT-4 for domain-specific tasks?

Fine-tuning for medical applications requires extra care. Here's my experience:

Data Preparation:

1. Quality over quantity: 10,000 cases is good, but ensure they're diverse and high-quality
2. Format consistency: Use a consistent prompt template
3. Data cleaning: Remove ambiguous cases, ensure diagnoses are verified
4. Train/validation split: 80/20 split, stratified by diagnosis type

Hyperparameters:

• Start with default learning rate (OpenAI auto-tunes this)
• Epochs: 3-4 (more can cause overfitting)
• Batch size: Let OpenAI handle this

Evaluation:

1. Domain-specific metrics: Accuracy, precision, recall for each diagnosis category
2. Human evaluation: Have medical professionals review outputs
3. Comparison baseline: Compare against base GPT-4 and other models
4. Edge cases: Test on rare conditions and ambiguous symptoms

Avoiding Overfitting:

• Use validation loss monitoring
• Early stopping if validation loss increases
• Data augmentation (rephrase symptoms)
• Regularization through diverse training examples

Critical for Medical AI:

• Always include disclaimers about not replacing professional medical advice
• Test thoroughly for bias across demographics
• Consider regulatory requirements (HIPAA, FDA)
• Implement confidence thresholds for uncertain diagnoses

I'd also recommend starting with GPT-3.5-turbo fine-tuning first (cheaper) to validate your approach before moving to GPT-4.