chain of thought (CoT)

Chain-of-Thought (CoT) prompting is a technique for large language models (LLMs) of sufficient scale in which the prompt is structured to encourage the model to generate intermediate reasoning steps (a chain of thought) before producing a final answer to a multi-step or complex problem.

The prompt typically includes either an instruction like Let’s think step by step or few-shot demonstration examples that explicitly show reasoning steps leading to the answer.

A notable variation, self-consistency, enhances CoT by sampling multiple reasoning paths and then selecting the answer that’s most consistent across those paths.

Reasoning models internalize this behavior. They’re trained with reinforcement learning to produce a chain of thought before answering, so explicit CoT prompting is less necessary when you use them.