When fine-tuning is actually the right tool
Fine-tuning teaches a model to behave a certain way by showing it many examples of the input-and-desired-output pairs. It shines for consistent style, tone, or format, and for narrow, repetitive tasks where you have lots of good examples of the right answer.
It is often not the right first move. If your goal is answering questions from your documents, RAG is usually better and cheaper. If good prompting already gets you there, fine-tuning adds cost and maintenance for little gain. We reach for it when prompting and retrieval have hit their limits.
- Good fit: enforcing a specific style, tone, or output format consistently
- Good fit: a narrow, repeated task with many examples of the correct output
- Good fit: shrinking long prompts by baking the behavior into the model
- Usually better as RAG: answering from a body of documents
- Usually better as prompting: one-off or low-volume tasks
- Not a fix for missing knowledge — that's what retrieval is for
What fine-tuning requires from you
Fine-tuning lives and dies on the training data. You need a meaningful number of high-quality examples of the exact behavior you want — clean, consistent, and representative of the real inputs the model will see. Garbage examples produce a model that's confidently wrong in your own style.
It's also not one-and-done. As your needs change you may need to retrain, and every version needs evaluation against a held-out test set to confirm it's actually better. We handle that loop and are honest when the data isn't there to make fine-tuning worth it yet.
- A solid set of high-quality input/output examples of the target behavior
- Consistency in those examples — mixed quality teaches mixed results
- A held-out test set to measure whether the tuned model is genuinely better
- A plan for retraining as your needs and data evolve
- Clear success criteria so 'better' is measured, not assumed
- Honest assessment first: whether prompting or RAG would get you there cheaper
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Frequently asked questions
Will fine-tuning make the model know our company's information?
Not reliably — that's a common misconception. Fine-tuning shapes behavior and style, not factual recall. For answering from your documents, RAG is the right tool. We'll steer you to whichever actually solves your problem.
How many examples do we need?
Enough high-quality, consistent examples to represent the behavior you want — the exact number depends on the task and how varied the inputs are. Quality matters more than raw count. If you don't have good data yet, we'll say so before spending on training.
Should we fine-tune or just improve our prompts?
Often, better prompting or RAG gets you most of the way at a fraction of the cost and maintenance. We recommend fine-tuning only when those approaches have hit a real ceiling and you have the data to support it.