Chain-of-thought prompting elicits reasoning in LLMs
Confirmed
Importance: major (4 of 5)The takeaway
Wei et al. showed that prompting large models to write out intermediate reasoning steps dramatically improves performance on math and logic tasks — an ability that emerges with scale.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- Major (4 of 5)
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- arXiv 2201.11903 (January 2022); NeurIPS 2022
- PaLM 540B with chain-of-thought reached state of the art on GSM8K math word problems at the time
- Follow-up: ‘Let’s think step by step’ zero-shot CoT (Kojima et al., 2022)
- Precursor to trained reasoning models like OpenAI o1
What happened
Adding worked examples with step-by-step reasoning in the prompt caused large models to reason explicitly before answering.
Why it matters
Made ‘thinking out loud’ central to LLM capability; RL-trained reasoning models (o1, R1, Claude extended thinking) are its descendants.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 2 primary
Primary
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (arXiv)arxiv.org, paper
- Language Models Perform Reasoning via Chain of Thought (Google Research blog)research.google, official
Changes
- Filed