Chain-of-thought prompting asks a language model to produce intermediate reasoning steps before its final answer. It can help on some multi-step tasks, but a coherent explanation is not proof that the answer is correct—or a faithful transcript of the model’s internal process.
What is chain-of-thought prompting?
Chain-of-thought (CoT) prompting is a technique for eliciting a sequence of natural-language steps between a question and an answer. In the original few-shot method, the prompt contains examples that pair an input with a reasoning sequence and a final output. The model is then asked a new question in that format.
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Unlike direct-answer prompting, which asks for an answer immediately, CoT supplies or requests intermediate steps. It changes the prompt and the model’s generated output; the method described in the original work does not require retraining the model or changing its weights. Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models” (2022); Google Research’s explanation.
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In ordinary use, “think step by step” is a request to show intermediate steps rather than return only a final result. For example, asked how many cars there are after three cars are joined by two more, a stepwise answer might identify the starting count, add two, and give five. The steps make the response easier to follow, but their presence alone does not establish that the model used valid reasoning.
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The original research method is more specific than a bare instruction: its few-shot examples demonstrate the desired format by showing an input, a chain of thought, and an output. A request for steps can elicit a similar-looking response, but that does not make every such prompt equivalent to the evaluated few-shot setup.
Does chain-of-thought prompting improve reasoning?
It improved performance on some multi-step arithmetic, commonsense, and symbolic reasoning benchmarks in the experiments reported by Wei and co-authors in 2022. The result was conditional, not universal: it depended on the evaluated tasks, prompts, and models. On the paper’s easiest single-operation subset, improvements were small or negative, so extra explanation is not by itself a reason to expect a better answer.
Model scale mattered in the 2022 experiments
The original study reported that gains emerged at approximately 100 billion parameters in its experiments; smaller models did not benefit positively there. With PaLM, a 540-billion-parameter model, eight chain-of-thought exemplars achieved state-of-the-art accuracy on GSM8K at the time. These are historical results for the models and benchmark settings in that paper—not a current minimum-size rule or a guarantee for models available today. Original paper; Google Research summary.
Self-consistency is a separate extension
Self-consistency samples multiple reasoning paths and selects the answer that is most consistent across them, rather than relying on one generated path. In their 2022 experiments, its authors reported gains on GSM8K, SVAMP, AQuA, StrategyQA, and ARC-challenge: respectively +17.9%, +11.0%, +12.2%, +6.4%, and +3.9%. Those figures describe reported benchmark gains in the authors’ tested settings; they are not expected improvements for every model or application. Wang et al., “Self-Consistency Improves Chain of Thought Reasoning in Language Models” (2022).
Can you trust an AI’s chain-of-thought explanation?
Use it as an explanation to inspect, not as a correctness certificate. A model can produce a fluent sequence of steps that contains an error, and a plausible rationale does not prove that the answer followed from those steps.
A 2022 empirical study found that demonstrations containing invalid reasoning retained over 80–90% of the performance of valid chain-of-thought demonstrations under the study’s metrics. That finding suggests performance can persist even when the demonstrated logic is flawed; it cautions against assuming that a plausible-looking explanation means the model followed valid logic. “Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters”.
Faithfulness is a distinct issue from answer accuracy. OpenAI’s 2024 o1 System Card discusses research on unfaithful explanations and describes a case in which a model’s chain-of-thought summary indicated awareness that it could not make up or find references, yet it then supplied references that appeared fabricated. Generated CoT should therefore not be treated as a transparent transcript of hidden cognition or as an audit guarantee. OpenAI o1 System Card.
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Research is also examining whether CoT can help people monitor model behavior. OpenAI’s 2025 evaluation reports that access to CoT improved monitoring in the settings tested. The authors describe those evaluations as relatively in-distribution math, science, and coding tasks; they also characterize follow-up-questioning results as preliminary and say more study is needed. This is evidence of a possible monitoring benefit in evaluated settings, not proof that CoT makes a model fully interpretable or reliably safe. OpenAI, “Evaluating chain-of-thought monitorability” (2025).
Quick Recap
How to use stepwise answers responsibly
- Check the final answer against the problem, a calculation, or an authoritative source appropriate to the task.
- Inspect each stated step for unsupported assumptions, arithmetic errors, or invented references.
- For consequential decisions, do not treat a detailed rationale as independent verification; seek evidence beyond the generated explanation.
- Expect longer output when intermediate steps are requested. Sampling multiple paths, as in self-consistency, also involves generating more than one response; the cited benchmark results do not quantify its cost for a particular use.
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