
Unlocking Creativity with Azure OpenAI
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Imagine a chef who specializes in a particular cuisine after extensive training. Fine-tuning is akin to this focused training, where the LLM is adjusted based on a curated dataset tailored to specific tasks. This dataset includes input-output pairs that clearly illustrate the task at hand and the expected results. Through this process, the model’s internal parameters are refined, enhancing its ability to perform specialized tasks. However, fine-tuning should be used with caution, as it requires significant computational resources and can be expensive. If not managed properly, it may lead to overfitting, where the model performs well on the fine-tuning dataset but poorly on other tasks, reducing its generalization ability. Additionally, fine-tuning can require substantial time and effort, so it should only be employed when necessary. In some cases, less resource-intensive approaches, such as prompt engineering or transfer learning, may be...