Fine-tuning is a research area in machine learning and artificial intelligence that involves adjusting the parameters of a pre-trained model to improve its performance on a specific task or dataset. This process is typically done by feeding new, task-specific data into the pre-trained model and fine-tuning its parameters through additional training. Fine-tuning is often used in transfer learning, where a model trained on a large, general dataset is adapted to a more specialized task. It allows for faster and more efficient training on new tasks, as the pre-trained model has already learned low-level features that can be useful for the new task. Fine-tuning is a powerful technique that can help improve the accuracy and performance of machine learning models in various applications.