Depending on the context, fine-tuning can have two distinct meanings.
a)
In the context of Large Language Models (LLMs), fine-tuning refers to the process of taking a pre-trained model and further training it on a specific dataset to enhance its performance. This technique is used to adapt a general-purpose model to a specific task or to improve its ability to understand specific nuances, contexts, or languages. Fine-tuning is a crucial step in Machine Learning and AI development, as it allows for more accurate and efficient models by leveraging existing neural networks and reducing the need for extensive training from scratch.
Training examples may be written by humans or generated as synthetic data. In model distillation, a student model is fine-tuned to reproduce selected behavior from a more capable teacher.
b)
In informal discussions of Prompt Engineering, "fine-tuning a prompt" sometimes means iteratively optimizing its wording. This should not be confused with model fine-tuning, which updates model parameters through training.
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