What enhancement does fine-tuning offer to a trained AI model?

Fine-tuning enhances a trained AI model by making targeted adjustments to improve its outputs. This process involves taking an already trained model, which has learned a general understanding of the data, and refining it on a more specific dataset related to a particular task or domain. Fine-tuning allows the model to adapt its existing knowledge to produce more accurate and relevant predictions or classifications without starting from scratch. By leveraging the foundational knowledge that was previously established, fine-tuning can significantly enhance performance on nuanced tasks, leading to improved results in applications such as natural language processing or image recognition.

This method is efficient as it requires less computational power and time compared to a complete re-training process, as it focuses on adjusting parameters rather than redefining them entirely.