AI Glossary
Chain-of-Thought Prompting
A prompting technique that encourages an AI model to reason through a problem step by step before providing an answer.
Chain-of-Thought Prompting
Overview
Sometimes solving a problem requires more than simply producing an answer.
Humans often work through challenges by breaking them into smaller steps. We examine information, consider possibilities, and gradually build toward a conclusion.
Chain-of-thought prompting applies a similar idea to AI systems.
This technique encourages a model to reason through a problem step by step before generating a final answer. Instead of jumping directly to a conclusion, the model is guided to work through intermediate reasoning.
A helpful way to think about chain-of-thought prompting is showing your work in a math class. The goal is not simply arriving at the answer but understanding the process used to reach it.
Chain-of-thought prompting became particularly important with modern Large Language Models (LLMs), helping improve performance on complex reasoning and problem-solving tasks.
Although users may not always see the reasoning process directly, the concept has influenced many advances in modern AI systems.
Why It Matters
Chain-of-thought prompting can improve reasoning and problem-solving performance in AI systems.
Real-World Example
A user asks an AI system to solve a business problem and instructs it to analyze the situation step by step before recommending a solution.