Definition
Fine-tuning gives a finished general-purpose AI model extra focused practice on your own examples so it gets better at one specific task, style, or domain.
At a glance
- You don’t build from scratch. It starts from an expensive finished model (GPT, Llama) and just nudges it[1] — like sending an experienced generalist on a short specialty course.
- It changes HOW the model answers (tone, format, behavior), not WHAT facts it knows. For changing facts, connect it to your documents (RAG) instead.
- It needs curated example pairs — typically hundreds to a few thousand. Quality beats volume; bad examples teach bad habits.
- Reach for it last. Most business goals are met by cheaper options first[5].
When to use it
Follow the cheaper-first rule: write better prompts, then add document retrieval (RAG) for your facts, and fine-tune only when you need a consistent style or behavior those two can’t deliver[2]. It pays off on narrow, repetitive, high-volume tasks where a locked-in voice or format saves real money and removes long instructions from every prompt[6].
The hidden costs
Pushing a model toward narrow examples can make it worse at general tasks — called catastrophic forgetting[4]. A custom model is also yours to maintain: when the base model upgrades, you may need to re-tune and re-test. Lightweight methods like LoRA adjust only a tiny slice of the model, cutting cost and reducing forgetting — the practical default today[3].
Bottom line
Fine-tuning is a focused upgrade, not a from-scratch build — the expensive last resort after prompting and retrieval, made practical by LoRA.