To address these limitations, the author introduces the Panel-of-Experts approach, which is a prompting method that encourages a panel-like discussion among different personas to introduce differing viewpoints and arguments. This approach has shown to improve reasoning and outcomes, reducing the error rate in their tests from 40% to 20%. However, the author notes that this approach doubles the cost of reviewing docstrings for a particular diff, but the overall costs are still small and the performance boost is significant.
Key takeaways:
- Large Language Models (LLMs) can perform complex tasks more reliably using Chain-of-Thought prompting, which involves the model laying out its reasoning process step-by-step.
- Despite its benefits, Chain-of-Thought prompting has limitations, particularly when the model fails to pay attention to negative instructions in the prompt.
- The Panel-of-Experts approach, an extension of the Tree-of-Thought approach, can improve the performance of LLMs by introducing differing viewpoints and arguments, leading to better reasoning and outcomes.
- While the Panel-of-Experts approach can significantly improve performance, it also roughly doubles the cost of reviewing docstrings for a particular diff, which is something to consider when using this method.