# Chain-of-Thought (CoT)

> Chain-of-thought prompting has a model work through intermediate reasoning steps before answering — improving accuracy on multi-step problems.

**Chain-of-thought (CoT) is the technique of having a language model produce intermediate reasoning steps before its final answer — decomposing a problem in writing instead of jumping to a conclusion.**

It works because generation is sequential: each reasoning token the model writes becomes context for the next, effectively giving the model scratch space. On arithmetic, logic, and multi-step planning, eliciting steps ("think step by step", or [few-shot examples](/glossary/few-shot-prompting) that demonstrate worked reasoning) historically delivered large accuracy gains.

Its 2026 status is nuanced: CoT *prompting* became less necessary as [reasoning models](/glossary/reasoning-model) internalized the behavior — they generate thinking tokens natively, and redundant "think step by step" instructions can just add cost. The technique still matters on non-reasoning tiers, in [LLM-as-judge](/glossary/llm-as-judge) rubrics where visible reasoning aids auditability, and as the conceptual ancestor of the reasoning-model era. When to reach for explicit CoT versus structure versus examples is mapped in [Few-Shot vs Chain-of-Thought vs Structured Prompting](/guides/prompting/prompting-techniques-2026).

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_Source: https://agentscamp.com/glossary/chain-of-thought — Term on AgentsCamp._