Chain-of-Thought Prompting
Chain-of-thought prompting asks a model to work through its reasoning step by step before giving a final answer. Spelling out the intermediate steps measurably improves accuracy on multi-step problems like math, logic, and planning.
Also known as: CoT prompting, chain of thought
Ask a model for just the answer to a multi-step problem and it often jumps to a wrong one. Chain-of-thought prompting instead asks it to reason through the steps first — “think step by step” — so it builds the answer on visible intermediate work. On math, logic, and planning tasks, that single change reliably raises accuracy, because the model isn’t trying to produce the conclusion in one leap.
Newer “reasoning models” bake this in, doing extended internal chains of thought before answering rather than needing to be prompted for it. The trade-off is cost and latency: more reasoning means more tokens and slower responses, so it’s worth it for hard problems and overkill for simple ones.