Chain of Thought (CoT)
A prompting technique that makes AI models show their reasoning step-by-step, leading to more accurate answers on complex problems.
What is Chain of Thought (CoT)?
Chain of Thought (CoT) is a prompting technique that gets AI models to break down their reasoning into steps before giving you an answer.
Instead of jumping straight to a conclusion, the model walks through its logic like you would on paper. This dramatically improves accuracy on math problems, logic puzzles, and multi-step tasks.
You can use it two ways: add examples that show step-by-step reasoning (few-shot CoT), or just add "Let's think step by step" to your prompt (zero-shot CoT). Most builders use it when they need the AI to solve something complex or when a direct answer keeps coming out wrong.
Works best with larger models like GPT-4 or Claude. The technique is built into newer reasoning models like OpenAI's o1, which automatically think through problems before responding.
Good to Know
Makes AI models explain their reasoning step-by-step instead of jumping to conclusions
Two main approaches: few-shot (show examples) or zero-shot (add 'Let's think step by step')
Dramatically improves accuracy on math, logic, and multi-step reasoning tasks
Works best with larger models like GPT-4, Claude, or specialized reasoning models
Built into newer AI reasoning models like OpenAI's o1 series
How Vibe Coders Use Chain of Thought (CoT)
Getting accurate answers to multi-step math problems by having the AI show its work
Debugging complex logic errors by asking the model to reason through each step
Building AI agents that need to plan sequences of actions before executing
Improving accuracy on coding problems that require understanding multiple file dependencies
Frequently Asked Questions
Related Terms
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