Fine-tuning is the process of further training a pre-trained AI model on a smaller, specific dataset to adapt it for a particular task or domain.
A foundation model is trained on broad general data. Fine-tuning adjusts its parameters using a focused dataset — for example, customer service transcripts or legal documents — so the model performs better on that specific type of content.
Fine-tuning does not guarantee accuracy. A fine-tuned model can still hallucinate or produce errors, particularly outside its fine-tuning domain.