Asked directly, a model sometimes jumps straight to a plausible-looking answer without actually working through the problem, the same way a person might blurt out a guess. Chain-of-thought prompts the model to lay out its intermediate reasoning first, step by step, before committing to a final answer, which measurably improves accuracy on multi-step problems like arithmetic, logic, and planning.
Some newer “reasoning” models now do a version of this internally by default, working through steps before producing output, rather than needing to be explicitly prompted to do so. Either way, the visible reasoning trail is also useful for a different reason: it gives a human reviewer something to actually check, rather than just a final answer to trust or distrust blindly.
It’s not a guarantee of correctness. A model can produce a fluent, structured chain of reasoning that arrives at the wrong answer, and the confident formatting can make that wrong answer more persuasive, not less. Reasoning steps are a useful signal, not proof of accuracy.