What is chain of thought?
Chain of thought is prompting a model to show its working: to lay out the intermediate steps of a problem before committing to an answer, instead of leaping straight to a conclusion. Asked to reason step by step, the model writes out the path it took, and only then states the result. The idea is that a hard problem solved in visible stages is less likely to go wrong than the same problem answered in one jump.
It tends to help most where the task has several steps that depend on each other, the kind of problem where a single skipped step quietly poisons the final answer. Writing the steps out gives the model a chance to catch that slip on the way, and gives you a chance to see where it went wrong if it did.
What does the reasoning cost?
The working is not free. Every reasoning step is more tokens generated, so a chain-of-thought answer is longer, slower, and eats more of the context window than a terse one. On an interactive setup that latency is real: a model that narrates its reasoning at length can turn a quick request into a wait.
So the trade is steps against speed. For a multi-step problem the extra tokens buy accuracy worth having. For a simple lookup they buy nothing but delay, and a plain answer is the better call. Some models reason at length by default, which makes the cost something you budget for rather than a setting you forget.