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Glossary · AI

Fine-tuning

Fine-tuning is further training an existing AI model on a set of your own examples so it learns a specific style, format or task more reliably than prompting alone can achieve.

In one line

Fine-tuning is further training an existing AI model on a set of your own examples so it learns a specific style, format or task more reliably than prompting alone can achieve.

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Why fine-tuning matters

Some tasks need very consistent output, a particular tone or a narrow skill. Fine-tuning can make a model better at these, and sometimes lets a smaller, cheaper model do the job.

How fine-tuning works

You prepare hundreds or thousands of high-quality input and output pairs. The model is trained further on them, producing a new version that leans towards those patterns.

The new model is then evaluated against the original to check it actually improved.

Common fine-tuning mistakes

Fine-tuning to add knowledge that changes often, which RAG handles better. Training on messy or inconsistent examples. Skipping evaluation, so nobody knows if the tuned model is better.

How Gr8r approaches fine-tuning

We usually try prompting and retrieval first because they are faster to change. We recommend fine-tuning only when evaluation shows a clear gap it can close, and we keep the training data and versions documented.

Example

A company fine-tunes a small model on several thousand approved product descriptions so new listings follow its house style and field structure without a long prompt each time.

FAQ

Questions

  • Should I fine-tune or use RAG?

    Use RAG to give the model facts that change. Use fine-tuning to shape behaviour, format or style. Some products use both.

  • Do I own a fine-tuned model?

    It depends on the provider's terms. Check who controls the weights, the training data and whether the model can be exported.

Related terms.

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