Chain-of-thought prompting
Also called CoT, step-by-step reasoning, think step by step
Asking the model to work through a problem step by step before giving its final answer.
Chain-of-thought prompting asks a model to write out intermediate reasoning before its answer. The phrase comes from a 2022 research paper showing that models solved more maths and logic problems correctly when examples included the working, not just the result. A simpler version, adding "think step by step" to a prompt, also helped.
It works because a language model produces text one token at a time. Writing the steps gives it room to compute partial results it can build on, instead of having to jump straight to the answer. It also makes errors visible, so you can see where the reasoning went wrong.
Reasoning models now do this internally: they are trained to think at length before replying, often in a hidden or summarised reasoning section. With these models, asking for step-by-step reasoning matters less, though asking to see key steps or assumptions is still useful for checking the work.
Example
"A shop sells pens at 3 for £2. How much do 10 pens cost? Work it out step by step, then give the answer on its own line." The model shows 9 pens cost £6, the 10th costs about 67p at the bundle rate, or £1 if sold singly, and asks which pricing applies.
Common mistake
Trusting the written reasoning as an exact record of how the model reached its answer. Research shows the stated steps do not always reflect what actually drove the output. Use the reasoning to spot errors, but still verify the final answer independently when it matters.