Extra training of an existing model on your own examples so it learns a specific style or task more reliably.
Fine-tuning means taking a pre-trained language model and training it further on your own set of examples, such as hundreds of conversations between your staff and customers. The result is a model that follows your style and required format more consistently without long instructions every time.
It is rarely the first step for small projects. It requires clean data, effort and cost, and must be repeated when information changes. If your goal is for the assistant to know your company's facts, RAG is usually faster and easier to keep up to date.
Practical tip: first try improving the system prompt and adding examples inside the prompt. Only when you hit the limits of that and behavior is still inconsistent should you consider fine-tuning, and prepare a test set to measure whether it actually helped.