When you do not need this

Fine tuning means training an existing model further on your own examples so it behaves differently. It is frequently the first thing people reach for and it is usually the wrong one.

Try these three first, in order, because they are cheaper and faster.

A better prompt. A large share of what people want from fine tuning is achieved by a clear instruction with examples in the prompt. Before anything else, spend a day on the prompt.

Examples in the prompt. Showing the model three or four examples of the input and the output you want changes behaviour substantially, costs nothing to change, and requires no training.

Retrieval. If the problem is that the model does not know your information, retrieval is the answer rather than fine tuning. This is the most common confusion in the whole subject: fine tuning teaches behaviour and style, and it is a poor way to teach facts.

Then fine tune when you need a consistent output format across thousands of calls, you need a specific tone or style that examples cannot reliably produce, you have a narrow repetitive task where a smaller cheaper model could do the job if trained, or your prompt has grown so long that it is expensive at volume.

And do not fine tune when your information changes, because retraining after every change is unworkable, or when you have fewer than a few hundred good examples, or when you have not yet measured whether the cheaper options work.

The honest summary: most business cases are a prompt problem or a retrieval problem wearing the costume of a training problem.