Fine-tuning: complete definition in AI for SMEs

Fine-tuning means adapting a pre-trained language model to your own data, to specialize it in a specific domain (your jargon, your tone, your processes). Unlike RAG, which "pastes" documents next to the model, fine-tuning modifies the model itself by further training it on a specific corpus.

When it helps (and when it does not)

Fine-tuning answers a specific need: a very specific style and vocabulary, constrained output formats, or reduced latency for frequent use. But for most SME use cases it is not required:

• RAG covers most needs (up-to-date data, sourcing);
• good prompt engineering and examples are often enough;
• fine-tuning adds cost, complexity and maintenance (retraining on fresh data).

The right reflex

Always start simple: structured prompt → RAG → and only fine-tune if the need truly demands it. An honest provider rarely recommends fine-tuning first: it pays off when dozens of repetitive uses justify the investment. That is the logic of an AI audit before any tuning spend.