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Chain-of-thought (CoT) prompting is a technique that asks a language model to generate intermediate reasoning steps before giving its final answer. It can help on some multi-step reasoning tasks, but a convincing explanation is not proof that the answer is correct or a faithful transcript of the model’s internal process.
What chain-of-thought prompting means
In ordinary prompting, a model may move directly from a question to an answer. CoT prompting instead encourages it to produce a sequence of intermediate natural-language steps on the way to that answer. The foundational method introduced by Jason Wei and co-authors used few-shot examples: each demonstration contained an input, a chain of thought, and an output. At inference, the model was given examples in that format and prompted to respond similarly. The 2022 paper defines these intermediate steps as a series of natural-language reasoning steps leading to an output.
“Think step by step” is a familiar short instruction for requesting this kind of response, but it is not the original method’s entire format. The original few-shot approach included worked examples with intermediate steps. Google Research describes CoT as prompting a model to produce intermediate reasoning steps before answering a multi-step problem. The technique changes the prompt, not the model’s weights, and does not require retraining for that purpose. Google Research’s explanation contrasts this with direct-answer prompting.
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Suppose the question is, “There are three cars, and two more arrive. How many cars are there?” A direct response might be just “5.” A stepwise response could state that there are three cars to start, two are added, and 3 + 2 = 5. This illustrates the format, not evidence that the explanation reliably reflects how the model arrived at its answer. A similar arithmetic example appears in the self-consistency paper.
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Few-shot CoT examples generally follow this structure:
- Input: Ask a question or present a problem.
- Intermediate steps: Show a sequence of steps leading toward an answer.
- Output: Give the final answer.
That format can make a response easier to inspect than a bare answer. However, seeing steps is different from verifying them: the steps may be incomplete, mistaken, or disconnected from the process that produced the answer.
Does chain-of-thought prompting improve reasoning?
It improved performance on some multi-step arithmetic, commonsense, and symbolic reasoning benchmarks in the original 2022 experiments. The size of the effect depended on the task and model scale. These results support a limited claim: CoT prompting can improve performance under particular experimental conditions. They do not establish that every model, prompt, or reasoning task will benefit.
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Scale was a notable condition. The original study reported that gains emerged at approximately 100 billion parameters in its experiments; it did not find positive effects for smaller models in those tests. Google Research summarized the same scale-dependent finding. That figure is a result from the 2022 study, not a current minimum-size rule: model architectures, training, and inference methods have changed since then. The paper also found that improvement was small or negative on its easiest single-operation subset, so extra generated steps alone are no reason to expect better answers to simple questions. The original paper reports the task results, while a later empirical study examines which properties of CoT demonstrations matter.
One result from the original paper concerned PaLM, a 540-billion-parameter model. With eight chain-of-thought exemplars, it achieved state-of-the-art accuracy on GSM8K at the time of that study. This describes a specific model, prompt setup, and benchmark result—not a guarantee about present-day systems or everyday use.
Self-consistency is a related extension
Self-consistency is a separate decoding approach that samples multiple reasoning paths and selects the most consistent answer, rather than relying on just one generated path. In their 2022 experiments, its authors reported gains of 17.9% on GSM8K, 11.0% on SVAMP, 12.2% on AQuA, 6.4% on StrategyQA, and 3.9% on ARC-challenge. These are reported benchmark gains in those experiments, not expected improvements for other models or tasks. Generating multiple paths also entails more inference than producing a single path; the cited results do not quantify that cost. The paper describes the method and its benchmark results.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you trust an AI’s chain-of-thought explanation?
Not as a correctness certificate or a transparent record of internal reasoning. A model can produce a coherent-looking sequence that contains a logical error, and the final answer still needs independent checking. In a 2022 study, invalid-reasoning demonstrations retained over 80–90% of the performance of valid chain-of-thought demonstrations under the study’s tested metrics. The finding suggests that strong performance does not necessarily mean the model followed valid logic; other properties, including relevance and ordering, also mattered. The empirical study reports those results.
Faithfulness is a distinct issue from accuracy. A generated rationale is not automatically a faithful account of the process behind an answer. OpenAI’s 2024 o1 System Card discusses research on unfaithful explanations and describes a case in which a chain-of-thought summary indicated that a model knew it could not make up or find references, yet the model supplied references that appeared fabricated. A rationale therefore cannot serve as an audit guarantee, especially when the answer involves claims that can be checked against external evidence.
Research is also exploring whether CoT can help monitor model behavior. OpenAI’s 2025 monitorability study reported that access to CoT improved monitoring in its evaluated settings. The article describes evaluations focused on relatively in-distribution math, science, and coding tasks, calls follow-up-questioning results preliminary, and notes that the approach needs more study. This is evidence of an active research direction, not proof that CoT makes models fully interpretable or reliably safe.
Quick Recap
How to use a stepwise answer
- Use intermediate steps when breaking down a genuinely multi-step problem could help you inspect the answer.
- Check the final result independently, particularly for arithmetic, factual claims, citations, and decisions with real consequences.
- Look for missing assumptions, invalid transitions, or steps that do not support the conclusion; polished wording does not make a rationale sound.
- Do not infer that a model’s explanation exposes its hidden internal process or guarantees that it used the stated steps.
- For a simple question, judge the answer on its merits rather than assuming that a longer response is more reliable.
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