# Fine-tuning

> Continuing to train an existing model on your own examples, so it adopts a behaviour or a style it did not have.

Fine-tuning changes how a model behaves. It does not reliably add knowledge, which is the misconception that wastes the most money in this area. If the problem is that the model does not know your product catalogue, retrieval solves it, stays current and costs a fraction. If the problem is that the model will not hold a house style, follow an unusual output format or match a visual look, fine-tuning is the right instrument.

Image models are where it earns its keep most visibly: a LoRA trained on a few dozen consistent images will hold a look across a whole set in a way no prompt reliably does. On the text side, the honest sequence is to exhaust prompting and retrieval first, because a fine-tune is a fixed asset that has to be maintained, re-run when the base model moves on, and evaluated against the thing it replaced.

## Related terms

- https://dfieldsolutions.com/en/glossary/lora.md
- https://dfieldsolutions.com/en/glossary/llm.md
- https://dfieldsolutions.com/en/glossary/rag.md

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Source: https://dfieldsolutions.com/en/glossary/fine-tuning
DField Solutions — Dunakeszi, Hungary — dezso@dfieldsolutions.com
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