Chain-of-Thought Prompting: When It Helps and When It's Just Noise

Chain-of-thought (asking the model to reason through steps before answering) reliably helps on tasks that have real intermediate structure: multi-step arithmetic, logic puzzles, planning, and non-trivial code. It reliably does not help — and sometimes hurts — on tasks that are pure retrieval or pattern-matching, where forcing extra "reasoning" just gives the model more room to talk itself into a wrong answer. How to use it well: - Ask for the reasoning as a means to an end, not the deliverable — "work through this step by step, then give me the final answer on its own line" beats an unstructured wall of reasoning you have to parse. - Don't bother on tasks a model already gets right zero-shot — you're just spending tokens. - If a task is failing, try chain-of-thought before trying a bigger model — it's cheaper and often fixes the same class of mistake. - With current reasoning-tuned models, explicit "think step by step" instructions matter less, since the model already does internal reasoning by default — test whether adding it changes anything before assuming it's needed.