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What the interviewer is testing
Whether you know when chain-of-thought is worth its cost, can describe how to use it in production, and understand that the written reasoning is not necessarily what drove the answer.
Answer framework
- Define it: ask the model to reason step by step before giving a final answer, or show examples that include reasoning.
- Where it helps: multi-step arithmetic, logic, planning and problems with several constraints.
- Where it does not: simple lookups, classification with clear labels, short rewrites.
- Costs: more output tokens, so higher cost and latency.
- Production pattern: separate the reasoning from a final structured answer, and do not treat the reasoning as a faithful explanation.
Sample answer
Chain-of-thought prompting means getting the model to write intermediate reasoning before the answer, either with an instruction such as asking it to work through the problem step by step, or with few-shot examples that show the reasoning. Because each generated token conditions the next, writing out steps gives the model room to compute what it cannot do in a single jump.
It helps most on tasks with several dependent steps: word problems, scheduling with constraints, multi-hop questions. It adds little to a sentiment label or a fact lookup, and it costs output tokens and latency every time.
In production I ask for the reasoning and a final answer in separate fields, parse only the final field, and often hide the reasoning from users. I also treat the reasoning as a useful debugging aid, not proof of how the model reached its answer, because it may not reflect the real computation. With models trained to reason internally, an explicit chain-of-thought prompt usually adds little.
Likely follow-ups
Common mistakes
- Adding chain-of-thought to every prompt by default.
- Parsing the answer out of free-form reasoning instead of a separate field.
- Treating the written reasoning as a faithful explanation.
- Forgetting the token cost at scale.
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Related questions
Based on question #18 in the full question bank.