---
title: "Chain-of-thought Prompting"
description: "Chain-of-thought prompting is a technique used in the field of Natural Language Processing, particularly with Large Language Models (LLMs), to enhance the model's reasoning capabilities by guiding it through a series of intermediate steps or thoughts. This method involves structuring prompts in a way that encourages the model to break down complex problems into smaller, manageable parts, thereby improving its ability to generate coherent and contextually relevant responses. By simulating a logical progression of ideas, chain-of-thought prompting helps LLMs tackle tasks that require multi-step reasoning, such as mathematical problem-solving, logical deduction, and complex decision-making processes. This approach leverages the model's capacity to follow a narrative or sequence, ultimately leading to more accurate and insightful outputs."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/chain-of-thought-prompting"
---

> Index of every page on this site: [llms.txt](https://promptmetheus.com/llms.txt)

Chain-of-thought prompting is a technique used in the field of [Natural Language Processing](https://promptmetheus.com/resources/llm-knowledge-base/natural-language-processing-nlp), particularly with [Large Language Models (LLMs)](https://promptmetheus.com/resources/llm-knowledge-base/large-language-model-llm), to enhance the model's reasoning capabilities by guiding it through a series of intermediate steps or thoughts. This method involves structuring [prompts](https://promptmetheus.com/resources/llm-knowledge-base/prompt) in a way that encourages the model to break down complex problems into smaller, manageable parts, thereby improving its ability to generate coherent and contextually relevant responses. By simulating a logical progression of ideas, chain-of-thought prompting helps LLMs tackle tasks that require multi-step reasoning, such as mathematical problem-solving, logical deduction, and complex decision-making processes. This approach leverages the model's capacity to follow a narrative or sequence, ultimately leading to more accurate and insightful outputs.

This prompting method is distinct from [chain-of-thought monitoring](https://promptmetheus.com/resources/llm-knowledge-base/chain-of-thought-monitoring), which evaluates available reasoning traces for signals about a model's behavior.

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