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Dictionary

Chain-of-Thought Prompting

Instructing a model to think step by step before answering.

1 min readupdated 2026-07-04

/ quick answer

CoT prompting adds 'let's think step by step' or a worked example, boosting accuracy on math, logic, and multi-hop reasoning at the cost of extra tokens. Instructing a model to think step by step before answering.

Instructing a model to think step by step before answering. CoT prompting adds 'let's think step by step' or a worked example, boosting accuracy on math, logic, and multi-hop reasoning at the cost of extra tokens. In practice: 'First list the constraints, then solve.' can lift accuracy by 20+ points on reasoning benchmarks. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
CoT prompting adds 'let's think step by step' or a worked example, boosting accuracy on math, logic, and multi-hop reasoning at the cost of extra tokens.
Example
'First list the constraints, then solve.' can lift accuracy by 20+ points on reasoning benchmarks.
/ frequently asked

What is Chain-of-Thought Prompting?

CoT prompting adds 'let's think step by step' or a worked example, boosting accuracy on math, logic, and multi-hop reasoning at the cost of extra tokens.

What is an example of Chain-of-Thought Prompting?

'First list the constraints, then solve.' can lift accuracy by 20+ points on reasoning benchmarks.

Why does Chain-of-Thought Prompting matter for AI and automation?

Instructing a model to think step by step before answering. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.