Chain of Thought
Prompting an LLM to reason step-by-step before answering, often improving accuracy on hard tasks.
/ quick answer
Chain-of-Thought (CoT) is a prompting technique where the model writes intermediate reasoning steps before the final answer. Newer 'reasoning' models (o-series, Claude thinking, Gemini thinking) run CoT internally. Prompting an LLM to reason step-by-step before answering, often improving accuracy on hard tasks.
/ frequently asked
What is Chain of Thought?
Chain-of-Thought (CoT) is a prompting technique where the model writes intermediate reasoning steps before the final answer. Newer 'reasoning' models (o-series, Claude thinking, Gemini thinking) run CoT internally.
What is an example of Chain of Thought?
Instead of 'What is 17 * 24?', prompt 'Think step by step, then answer. What is 17 * 24?' — the model breaks it into partial products and gets the correct 408.
Why does Chain of Thought matter?
Prompting an LLM to reason step-by-step before answering, often improving accuracy on hard tasks.
What should I learn after Chain of Thought?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Chain-of-Thought Prompting
Instructing a model to think step by step before answering.
- →Few-Shot Prompting
Showing the model 2–5 examples of the task inside the prompt so it mirrors the pattern.
- →Zero-Shot Prompting
Asking a model to do a task with no examples in the prompt.
- →Prompt Chaining
Pipelining LLM calls where each step's output feeds the next.
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The tools that run it in production.
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