chain of thought prompts

Chain of thought (CoT) prompting is a technique that enhances the reasoning capabilities of large language models (LLMs) by encouraging them to break down complex tasks into smaller, more manageable steps. This approach allows the model to demonstrate its thought process, making it more transparent and easier to understand.

CoT prompting involves providing the model with a problem or question, along with a few examples of how to break down the problem into smaller steps and arrive at a solution. This can be done through few-shot learning, where the model is given a few examples to learn from, or through zero-shot learning, where the model is simply instructed to think step-by-step.

By using CoT prompting, LLMs can be better equipped to handle complex tasks that require multiple steps of reasoning, such as mathematical word problems, logical puzzles, and creative writing. This technique has the potential to significantly improve the performance of LLMs on a wide range of tasks.