Chain-of-thought (CoT)

Getting a model to work through intermediate steps before giving its final answer, the same way you might show your work on a math problem. Consistently improves accuracy on multi-step tasks.

Chain-of-thought (CoT) is getting a model to work through intermediate steps before giving its final answer, consistently improving accuracy on multi-step tasks. It works because each intermediate step becomes context for the next one, reducing the chance the model loses track of where it is in a complex problem. Ask a model to solve "what is 17 times 24" directly and it might guess wrong. Prompt it to multiply step by step and it performs far more reliably. Modern reasoning models like Claude with extended thinking and OpenAI's o-series do this automatically, sometimes showing a summary of their work and sometimes hiding it entirely.

Builder example

For straightforward tasks like classification or simple extraction, CoT adds latency and cost with little benefit. For multi-step math, logic, planning, or code generation, it can be the difference between a usable system and a broken one. Know which category your task falls into before deciding whether to request step-by-step reasoning.

You ask for a customer onboarding guide and the model jumps straight into prose.

Have it outline the customer's starting point, blocker, example, and success check before drafting.

Common confusion: The steps a model shows you may look logical yet still contain errors or post-hoc rationalization. Visible reasoning helps with debugging, but it does not prove the answer is correct.