What is Chain of Thought

Chain-of-thought (CoT) prompting is a technique used with generative AI models to elicit more detailed and reasoned responses to complex queries. Unlike standard prompting where the AI directly outputs a final answer, CoT prompting encourages the model to explain its step-by-step reasoning process in reaching the solution. This is achieved by instructing the AI to break down its thought process or "show its work" within a single prompt and response.

For example, consider a prompt asking an AI to calculate how long it would take an HR team to complete a mix of senior and junior employee evaluations with different time requirements. A standard prompt might simply state the total time as a final answer. However, a CoT prompt would ask the AI to detail the full calculation, such as first determining the total time for senior evaluations, then for junior, adding the prep time, and finally summing it all together for the total duration. By laying out each reasoning step, CoT prompts can help users better understand how the AI arrives at its conclusions.

CoT prompting has been found to improve the performance and accuracy of generative AI models on complex reasoning tasks, particularly in domains like mathematics, coding, and question-answering. While similar to prompt chaining in walking through multi-step problems, CoT is distinct in that it elicits the step-by-step reasoning in a single prompt and response, rather than across multiple prompts in a chain. However, both techniques leverage the power of large language models to go beyond simple queries and tackle more intricate, real-world problems. As generative AI continues to advance, CoT prompting will be a valuable tool in extracting more nuanced and transparent reasoning from these increasingly capable models.