Fine-tuning

Fine-tuning is the process of continuing the training of a pre-trained model on a smaller, task-specific dataset to specialise its behaviour.

What is Fine-tuning?

Fine-tuning updates some or all of a model's weights using supervised examples. Variants include full fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), instruction tuning, and reinforcement learning from human feedback. Fine-tuning is most useful for fixed style, format, or domain vocabulary that prompting cannot reliably capture. It is rarely the right answer for adding new factual knowledge, where retrieval-augmented generation is cheaper, more auditable, and easier to update.

How does Fine-tuning apply to enterprise AI?

Enterprises fine-tune to enforce tone-of-voice, regulated output formats, multilingual code-switching, or domain jargon. Fine-tuning data must be governed under GDPR and the EU AI Act, including consent, retention, and the right to erasure.