When Fine-Tuning Makes Sense
Fine-tuning trains an existing model on your specific data to improve performance on your tasks. It makes sense when: prompt engineering is not enough, you need consistent domain-specific behavior, you want to reduce token usage by encoding knowledge into the model, or you need to match a specific output format reliably.
It does NOT make sense when: your task is well-served by a general model with good prompts, your data is too small (under a few hundred examples), or your requirements change frequently.
Preparing Your Data
Fine-tuning data should be representative of your actual use case. Create input-output pairs that demonstrate exactly what you want the model to do. Quality matters more than quantity — 500 high-quality examples often outperform 5,000 mediocre ones.
Clean your data rigorously: remove duplicates, fix formatting inconsistencies, and ensure labels are accurate. Include examples of edge cases you want the model to handle correctly.
The Fine-Tuning Process
Choose a base model appropriate for your task and budget. Smaller models fine-tune faster and cheaper. Upload your data to the provider's platform (OpenAI, Anthropic, or an open-source framework). Configure hyperparameters (learning rate, epochs) — start with defaults.
Monitor training metrics and evaluate on a held-out test set. Good fine-tuning improves task-specific performance without degrading general capabilities.
After Fine-Tuning
Compare your fine-tuned model against the base model with good prompts. Sometimes the improvement is marginal, in which case prompt engineering is the simpler path. If the fine-tuned model wins, deploy it and continue evaluating on real-world data.
For the underlying concepts, see our transfer learning explainer.