Fine-tuning adjusts a model's weights using a curated set of examples, such as documents classified the right way or replies written in a house style. It can improve consistency on narrow tasks and reduce the instructions needed in each request.
It is not always the right tool. Many business needs are better met by retrieval, which keeps knowledge current, or by clearer instructions and evaluation. Fine-tuning requires good training data, ongoing evaluation, and a plan for retraining when the base model changes.