Fine-tuning refers to taking a pre-trained model and adapting it to a new task by continuing its training on a smaller, task-specific dataset. In AI, especially in natural language processing and computer vision, this approach leverages large models like GPT, BERT, or ResNet, making them more efficient for specialized applications without requiring vast computing resources.