Chain-of-thought reasoning is a technique where a model breaks a problem into intermediate steps before producing a final answer, which can improve performance on multi-step tasks like math, logic, or planning. In practice, those intermediate steps help the model keep track of assumptions and dependencies, reducing “jump to the conclusion” errors - though many systems now keep the full step-by-step internal and only expose a concise final explanation.