[Reasoning]·PAP-LRKYVF·March 17, 2026·★ Essential·Free Preview

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Jason Wei, Xuezhi Wang, Dale Schuurmans et al.

REASONING

4 min readReasoning

Core Insight

Chain-of-Thought Prompting elevates reasoning in LLMs, outperforming finetuned GPT-3 on complex math tasks.

Origin Story

arXiv preprintGoogle BrainJason Wei, Dale Schuurmans et al.

The Room

A group of researchers at Google Brain, 2022. They are driven by an itch — the nagging feeling that despite the massive scale of models like GPT-3, something is still missing. The room buzzes with the murmur of keyboards and the rustle of papers — minds wrestling with the limitations of logical reasoning in these otherwise powerful models.

The Bet

Instead of more training data or tweaks to architecture, they bet on a novel idea: using the model's own language to guide its reasoning. A simple yet daring contrarian move. There were moments of doubt, especially when some results seemed counterintuitive, but they held fast, believing in the potential of prompting itself to unlock untapped reasoning ability.

The Blast Radius

Without this work, models like PaLM and GPT-4 might have remained limited in reasoning, unable to tackle complex tasks with the same efficacy. The reverberations have been profound, influencing how researchers think about prompting and reasoning in AI. Key authors have continued to explore this frontier, pushing the boundaries of what language models can achieve and inspiring a new generation of research.

↳PaLM↳GPT-4↳Claude AI

Knowledge Prerequisites

git blame for knowledge

To fully understand Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, trace this dependency chain first. Papers in our library are linked — click to read them.

DIRECT PREREQIN LIBRARY

Attention Is All You Need

Understanding how transformers function is crucial for appreciating reasoning capabilities in large language models.

attention mechanismtransformer architectureself-attention

DIRECT PREREQIN LIBRARY

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

This paper introduces early advancements in transformer-based language models, foundational for understanding how large models evolve reasoning skills.

bidirectional transformersmasked language modelingtransfer learning

DIRECT PREREQIN LIBRARY

Language Models are Few-Shot Learners

Understanding few-shot learning helps grasp how large language models can adapt their reasoning processes without extensive training.

few-shot learningin-context learningprompting

DIRECT PREREQIN LIBRARY

Training Compute-Optimal Large Language Models

This paper discusses the methods to efficiently train large models, which directly affects their ability to perform complex reasoning tasks.

compute efficiencymodel scalingtraining optimization

DIRECT PREREQIN LIBRARY

Self-Consistency Improves Chain of Thought Reasoning in Language Models

The concept of self-consistency is a refinement of existing chain-of-thought methods, critical for an advanced understanding of reasoning improvements.

self-consistencychain of thoughtreasoning enhancement

YOU ARE HERE

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

By the Numbers

540B

parameters in PaLM model

examples required for effective prompting

GSM8K

benchmark where PaLM excelled

outperformed finetuned GPT-3

comparison on complex math tasks

In Plain English

The paper demonstrates 'Chain-of-Thought' prompting, improving large language models' reasoning via intermediate steps. A PaLM 540B model excelled on GSM8K math benchmarks, even besting finetuned GPT-3 with a verifier.

Explained Through an Analogy

“

Imagine teaching someone to cook not with a full recipe but with each little step—a pinch of salt here, a stir there—only to see them perfect a gourmet dish. That's chain-of-thought prompting for language models, letting them illuminate paths through reasoning as a chef navigates flavors.

The Full Story

~1 min · 213 words

The Context

What problem were they solving?

hain-of-thought prompting involves breaking down a question into logical steps so the model can better understand and solve it.

The Breakthrough

What did they actually do?

The scaling property of language models is key; larger models show emergent improved reasoning abilities.

Under the Hood

How does it work?

By using just a few thoughtful examples, model performance can surpass that of more complex finetuned models.

World & Industry Impact

This development opens new horizons for AI applications requiring advanced reasoning, like complex Q&A, tutoring systems, and decision support tools. Companies like Google, Microsoft, or startups innovating in AI-driven education could directly integrate chain-of-thought prompting to improve model accuracy and interpretability. This approach can redefine how we interact with AI, making them more effective collaborators in tasks demanding nuanced understanding.

Highlighted Passages

Verbatim lines from the paper — the sentences that carry the most weight.

“Chain-of-thought prompting involves guiding large language models through a series of reasoning steps, rather than asking for a direct answer.”
→ This highlights the importance of structured reasoning, which is crucial for product teams designing AI systems requiring deep interpretative skills.

“What sets this apart is its operational simplicity: just eight examples can transform the model's reasoning prowess.”
→ This operational efficiency can significantly reduce development time and resources for PMs aiming to implement advanced AI functionalities.

“Key results include a PaLM 540B model outperforming existing state-of-the-art methods on GSM8K math word problems.”
→ This achievement underlines the competitive edge possible through strategic prompting, essential for PMs aiming to achieve breakthrough product performance.

Use Cases for Your Product

How this research maps to real product scenarios.

Incorporate chain-of-thought prompting to improve the model's ability to handle complex customer queries, enhancing user satisfaction.

Utilize chain-of-thought prompting to improve risk assessment models, providing more accurate predictions and better financial advice.

Implement chain-of-thought prompting to enhance tutoring systems' capability to solve and explain complex problems, setting a new standard in educational AI tools.

Your PM Action Plan

Three concrete moves, prioritised by urgency.

Implement chain-of-thought prompting in current AI models to enhance reasoning capabilities

This quarter

Evaluate current model performance on complex reasoning tasks and benchmark against PaLM 540B results

This week

Prepare a presentation for stakeholders on the potential of chain-of-thought prompting to improve AI-driven applications

Watch closely

Experience It

Live Experiment

Chain-of-Thought Prompting

See Chain-of-Thought in Action

Wei et al. showed that asking a model to "think step by step" dramatically improves reasoning on math, logic, and common-sense problems. Enter any puzzle and see the difference yourself.

Pick an example — annotated before/after in seconds

Your reasoning problem — or write your own

⌘↵ to run

Talking Points for Your Next Meeting

Leverage chain-of-thought to refine AI reasoning in complex tasks.

Prompt models using strategic examples for improved problem-solving performance.

Recognize model scale's role in enabling emergent reasoning capabilities.

Click any point to copy — ready to paste into Slack, email, or your next deck.

Test Your Edge

You've read everything. Now see how much actually stuck.

Question 1 of 3

What key advantage does chain-of-thought prompting provide to large language models?

Question 2 of 3

How many examples are typically needed to effectively implement chain-of-thought prompting?

Question 3 of 3

Which model outperformed the finetuned GPT-3 on the GSM8K benchmark?

Read Original Paper on arXiv

How grounded is this content?

Metrics are computed from available source text only — abstract, summary, and impact fields ingested into this system. Full paper PDF is not ingested; numerical claims that originate from within the paper body will not appear in these scores.

Source Richness100%

8 of 8 content fields populated. More fields = better-grounded generation.

Source Depth~223 words

Total source text analyzed by the model. Includes extended deep-dive summary — high confidence.

Number Grounding4 / 4

Key statistics whose numeric values appear verbatim in ingested source text. Unverified stats may originate from the full paper body.

Quote Traceability3 / 3

Key passages whose significant vocabulary (≥4-char words) overlap ≥35% with source text. Measures lexical traceability, not semantic accuracy.

Methodology: Number grounding uses regex digit extraction against source text. Quote traceability uses token set intersection on content words stripped of stop-words. Neither metric validates semantic correctness or factual accuracy against the original paper. For full verification, cross-reference with the original paper via the arXiv link above.

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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

The Context

The Breakthrough

Under the Hood

See Chain-of-Thought in Action

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