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.
b)
In the context of Prompt Engineering, fine-tuning refers to the process of optimizing a prompt to achieve better results for a given task. This process involves experimenting with different prompts, parameters, and techniques to find the most effective way to make an LLM produce the desired outputs.
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