What it means
Fine-tuning is a training step where a pre-trained model (say Llama, Qwen, or GPT) gets additional training on a curated dataset from your business: hundreds or thousands of example inputs and outputs you consider correct. The model's weights shift to favour your style.
Fine-tuning is most useful when prompt engineering has hit a ceiling: the model needs domain knowledge or style that does not fit in the prompt, or you want to reduce the prompt size (and therefore cost) by baking the instructions into the weights. It is least useful when the underlying capability is missing; fine-tuning teaches style, not new skills.
Why it matters
For most SMEs, fine-tuning is not the first move. RAG with good prompts gets you 80 percent of the value at 20 percent of the effort. Fine-tuning becomes worth it later, when you have logs, labels, and a clear gap between what the base model produces and what your business needs.
It is also worth saying that the cost of fine-tuning has dropped dramatically. Open-source models like Qwen 3.6 can be fine-tuned on a single GPU rig for the price of a few days of API spend. The question is whether the result is materially better, not whether it is affordable.
Example
A specialty insurance broker fine-tunes a Qwen 3.6 model on 2,400 historical policy summaries written by their senior underwriters. The fine-tuned model now drafts policy summaries in the firm's house style on the first try, where the base model needed three rounds of editing. The fine-tune cost SGD 4,200 and saved each underwriter six hours a week.