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.
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 for AI and automation?
Prompting an LLM to reason step-by-step before answering, often improving accuracy on hard tasks. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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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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