When and How to Fine-Tune LLMs

Fine-tuning allows you to adapt a pre-trained language model to your specific domain or task. But it's not always the right choice. This guide helps you decide when to fine-tune and how to do it effectively.

When to Consider Fine-Tuning

Fine-tuning makes sense when:

• You need consistent output format or style
• Your domain has specialized vocabulary
• RAG alone doesn't achieve required accuracy
• You have high-quality training data

When to Avoid Fine-Tuning

Consider alternatives when:

• Your knowledge base changes frequently (use RAG instead)
• You lack sufficient training data
• You need to cite sources (use RAG)
• Prompt engineering achieves acceptable results

The Fine-Tuning Process

1. Prepare Your Dataset

The quality of your fine-tuned model depends entirely on your data:

{
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is our return policy?"},
    {"role": "assistant", "content": "Our return policy allows..."}
  ]
}

2. Choose Your Base Model

Consider:

• Task requirements (reasoning, generation, classification)
• Context length needs
• Cost constraints
• Deployment requirements

3. Configure Training

Key hyperparameters:

• Epochs: 2-4 typically sufficient
• Learning rate: Start with 1e-5
• Batch size: Based on memory constraints

4. Evaluate Results

Use held-out test data to measure:

• Task-specific accuracy
• Response quality
• Latency impact

Best Practices

1. Start with prompting: Exhaust prompt engineering first
2. Quality over quantity: 100 great examples beat 10,000 mediocre ones
3. Diverse examples: Cover edge cases in training data
4. Version control: Track datasets and model versions
5. Monitor drift: Performance can degrade over time

Cost Considerations

Fine-tuning costs include:

• Training compute
• Inference (often more expensive than base models)
• Data preparation time
• Ongoing maintenance

Conclusion

Fine-tuning is a powerful tool but not always the best solution. Carefully evaluate your requirements and consider simpler approaches like prompt engineering or RAG before investing in fine-tuning.