LLMInference & InterfacesUpdated 2026.04.28

Chain-of-Thought

Chain-of-Thought Prompting (CoT)

Also known asCoT단계별 추론 프롬프트

In one line

Chain-of-Thought (CoT) prompting asks the LLM to walk through intermediate reasoning steps before giving a final answer — a simple change that meaningfully improves accuracy on harder problems.

Going deeper

Chain-of-Thought prompting nudges the model to spell out intermediate reasoning before answering — sometimes literally with phrases like 'Let's think step by step'. Multiple papers have shown sizable accuracy gains on math, logic and multi-step problems.

Marketers do not invoke it constantly, but it is the rationale behind asking AI for analysis 'with reasoning shown'. Demand only a conclusion and you get plausible-sounding guesses; ask for the steps and consistency improves.

Reasoning-tuned models (GPT-5's reasoning modes, Claude's extended thinking, etc.) now perform CoT internally. You no longer need to spell out 'step by step' — the model will, by default, when the task warrants it.

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