``` When to use fine-tuning: Fine-tuning GPT models can make them better for specific applications, but it requires a careful investment of time and effort. We

``` When to use fine-tuning: Fine-tuning GPT models can make them better for specific applications, but it requires a careful investment of time and effort. We recommend first attempting to get good results with prompt engineering, prompt chaining (breaking complex tasks into multiple prompts), and function calling, with the key reasons being: * There are many tasks for which our models may initially appear to not perform well at, but with better prompting we can achieve much better results and potentially not need to be fine-tune * Iterating over prompts and other tactics has a much faster feedback loop than iterating with fine-tuning, which requires creating datasets and running training jobs * In cases where fine-tuning is still necessary, initial prompt engineering work is not wasted - we typically see best results when using a good prompt in the fine-tuning data (or combining prompt chaining / tool use with fine-tuning) ```