Fine-tuning is additional training applied to a language model on a specific set of examples, so it performs better on a narrow task.
Rather than relying on general training alone, fine-tuning adjusts a model using examples specific to a business or task, such as past customer replies in a company's own voice. Most installed systems rely on retrieval and clear prompting rather than fine-tuning, because it is usually a heavier lift than the task requires.