AI Fundamentals

Chain-of-Thought (CoT)

A prompting or training pattern where the model produces intermediate reasoning steps before its final answer, typically improving accuracy on multi-step tasks. Modern reasoning models (o1, o3, Claude with extended thinking) internalize CoT during training.

Related terms

ReAct
A prompting pattern for agents that interleaves Reasoning (thought) and Action (tool call), with each observation feeding the next thought. Foundational technique behind most early LLM agents; largely subsumed by more structured agent frameworks in 2025-2026.
Reasoning Model
A class of LLMs (OpenAI o-series, Claude with extended thinking, DeepSeek-R1) trained to spend significant inference-time compute on internal reasoning before producing the final answer. Substantially better on math, coding, and multi-step problems at the cost of higher latency and token consumption.

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Where this fits

Chain-of-Thought (CoT) is part of the AI Fundamentals vocabulary used in the Generative AI Maturity Framework. See the full glossary for the complete set of 149 defined terms, or take the free maturity assessment to see where your organisation stands.