HomeResearch LibraryChain-of-Thought Prompting Elicits Reasoning in Large L…
empirical studyChapter 1NeurIPS 2022 · 2022

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

Jason Wei (Google Brain), Xuezhi Wang (Google Brain)

Abstract

We explore how generating a chain of thought — a series of intermediate reasoning steps — significantly improves the ability of large language models to perform complex reasoning.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Chain-of-thought is the foundational prompting technique that made complex agent reasoning possible. Every subsequent reasoning technique — ToT, ReAct, Reflexion — builds on this insight.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Chain-of-thought prompting
  • →Emergent reasoning capability
  • →Multi-step problem solving

Topics

chain of thoughtreasoningpromptingmulti-step reasoning