Fine-tuning adapts a pre-trained LLM to specific tasks by training on domain-specific data. It changes model weights to improve performance on your use case.
When to Fine-Tune
- Specific output format or style needed
- Domain-specific terminology
- Consistent behavior patterns
- Prompting alone isn't sufficient
Fine-Tuning vs Alternatives
- Prompting: Fastest, no training needed
- RAG: Add knowledge without retraining
- Fine-tuning: Change model behavior fundamentally
Does fine-tuning prevent hallucinations?
Not necessarily. Fine-tuned models can still hallucinate. Production monitoring remains essential.
Practical example
A team trains a model on reviewed examples of its required extraction format.
What to check
Hold out representative tasks and compare against prompt-only and retrieval baselines before rollout.
Limits and tradeoffs
Fine-tuning does not automatically supply current facts, fix authorization, or eliminate unsupported answers.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.