Fine-Tuning LLMs: When and How to Customize AI Models

1-minute read
fine-tuning • llm • ai • machine-learning

Fine-tuning adapts a base model to your domain with labeled examples. Use it when prompting and RAG cannot achieve consistent style, format, or task-specific behavior.

When to Fine-Tune

  • Fixed output schema (legal clauses, medical codes)
  • Brand voice across thousands of responses
  • Specialized terminology poorly covered by general models

When Not to Fine-Tune

  • Facts that change frequently (use RAG)
  • One-off tasks (use prompting)
  • Small datasets without validation (risk overfitting)

OpenAI Fine-Tuning Flow

Prepare JSONL with messages arrays. Upload, create job, evaluate on a holdout set. Monitor loss and human ratings before promoting to production.

Conclusion

Fine-tuning is expensive to maintain as base models improve. Start with RAG + strong prompts; fine-tune only with clear metrics that generic approaches miss.