Fine-tuning

In fine-tuning, an existing model is trained further on a smaller, targeted dataset – for example with examples of a company’s tone of voice or a particular answer format.

For current or frequently changing knowledge, fine-tuning is usually not the best tool; RAG is better suited. Fine-tuning pays off when behaviour, format or terminology should be adapted permanently.