“Data fine-tuning” in AI models refers to the process of adjusting a pre-trained model with additional training data to optimize its performance on specific tasks. This allows the model to better understand nuances and provide more accurate outputs, particularly in specialized domains. Fine-tuning is crucial for adapting large models, like those based on transformer architectures, to cater to specific applications while leveraging general knowledge learned during initial training.