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Chain-of-thought (CoT) prompting asks a large language model (LLM) to produce intermediate reasoning steps before its final answer. It can improve performance on some multi-step tasks, but results depend on the model, task, and prompt—and a persuasive written explanation is not proof that the model reasoned faithfully.
What chain-of-thought prompting means
In CoT prompting, a model receives a question along with, or followed by, a request to show intermediate steps before answering. In the few-shot form, the prompt includes worked examples pairing a question with a rationale and answer. The model can then follow that demonstrated structure without changing its weights or undergoing fine-tuning. [Wei et al., 2022; Google Research]
The phrase “think step by step” is commonly associated with a zero-shot version: add an instruction to a question without supplying worked examples. That is different from few-shot CoT, which demonstrates the desired approach in the prompt. [Auto-CoT, 2022]
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The original CoT study, published by Jason Wei and coauthors at NeurIPS 2022, evaluated three language models on arithmetic, commonsense, and symbolic reasoning tasks. Its authors reported improvements across a range of those tasks; that finding is evidence for the tested settings, not a universal performance guarantee. [Wei et al., 2022]
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One specific result illustrates both the potential and the limits of the claim: on GSM8K, PaLM 540B achieved a 57% solve rate with CoT prompting using eight exemplars, compared with 18% using standard prompting in the study. Those figures apply to that model, benchmark, prompt setup, and experiment—not to every LLM or real-world problem. [Wei et al., 2022, Figure 2]
How to choose and build a prompt
Try zero-shot CoT for a quick baseline
For an initial test, ask the model to solve the task step by step, then give its final answer. This requires no demonstrations, but the cited evidence does not establish that a brief instruction works as well as carefully chosen examples across tasks. Treat the phrase as a prompt variant to test, not a magic switch. [Auto-CoT, 2022]
Use few-shot examples when the task needs a pattern
When the output should follow a particular sequence or reasoning format, provide relevant worked examples that show the question, intermediate steps, and answer. Make the steps clear and put them in a sensible order. A 2023 ACL study found that query relevance and correct step ordering mattered; its experiments also found invalid demonstrations retained over 80–90% of performance under various study metrics. That bounded result does not mean errors in examples are harmless for every task. [Wang et al., 2023]
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Automated methods such as Auto-CoT can generate demonstrations by clustering questions and sampling representative examples. But generated chains can contain mistakes, so inspect them before relying on them as examples. [Auto-CoT, 2022]
Compare the approaches on your own task
Test direct-answer prompting against zero-shot and, where appropriate, few-shot CoT using representative inputs. Record whether the final answers are correct separately from whether the written steps are coherent. If using examples, check their relevance and ordering, and review any automatically generated demonstrations. These comparisons help establish whether the added prompt structure benefits your task; the published results do not identify one best current model or universally effective wording.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why a readable rationale is not proof
A chain that sounds like a reasonable explanation is not necessarily a faithful record of what caused the model’s answer. In a 2023 study, Anthropic researchers intervened in chains of thought—for example, by inserting mistakes or paraphrasing—and examined how predictions changed. Reliance on stated reasoning varied by task; on most tasks they studied, larger and more capable models produced less faithful reasoning. CoT therefore should not be treated as transparency, an audit trail, or a safety guarantee merely because the model provides steps. [Turpin et al., 2023]
For high-stakes decisions or work that must be auditable, check answers against external evidence or deterministic validation rather than treating the rationale as verification. Evaluate the answer and the explanation as separate outputs.
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