Chain of thought is a powerful prompting technique used with large language models. Instead of jumping directly to an answer, it encourages the model to show its reasoning process step by step, much like how humans think through complex problems.
The mechanism works by prompting the model to break down complex problems into smaller, manageable steps. This can be implemented through few-shot prompting, where you provide examples with intermediate steps, or zero-shot prompting by simply adding phrases like "Let's think step by step" to your prompt.
Let's see chain of thought in action with a math problem. When asked to calculate 23 times 47, a model without chain of thought might just output 1081. But with chain of thought, the model shows its work: breaking down 47 into 40 plus 7, using the distributive property, calculating each part separately, and then adding them together. This transparency makes the reasoning process visible and verifiable.
Chain of thought provides several key benefits. It significantly improves accuracy on reasoning tasks, enables better problem decomposition, and makes the decision-making process more transparent. This technique shows enhanced performance across arithmetic problems, logical reasoning, multi-step questions, and complex analysis tasks. The transparency aspect is particularly valuable as it allows us to verify and understand how the model reaches its conclusions.