HomeResearch LibraryChain-of-Thought Prompting Elicits Reasoning in Large L…
Chapter 1 · 2022

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

Jason Wei, Xuezhi Wang, Dale Schuurmans

Abstract

Chain-of-thought prompting enables LLMs to solve complex reasoning tasks by generating intermediate reasoning steps, dramatically improving performance on arithmetic, commonsense, and symbolic 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 atomic unit of Zone III reasoning. Every complex enterprise workflow can be decomposed into a chain of reasoning steps. The key insight — that making the reasoning visible dramatically improves accuracy — is foundational for Zone III auditability. An agent that shows its work is not just more accurate; it is auditable, debuggable, and governable.

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.

Topics

chain of thoughtreasoningpromptingintermediate steps