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In this tutorial, we explore the process of fine-tuning a model in GPT-3. The video may be longer than usual, but the host believes it's necessary for viewers to gain valuable insights. Fine-tuning aims to create a consistent output that meets set criteria every time the model is run. This includes factors like length and text structure. To fine-tune a model, you need relevant data to train it effectively. The host begins by creating a desired output with a good prompt, emphasizing the importance of consistency in output format.
