What is Chain-of-Thought Prompting?
A technique where you instruct an AI to reason through a problem step by step before giving its final answer, improving accuracy on complex tasks.
Definition
Chain-of-thought (CoT) prompting is a technique where you ask an LLM to show its reasoning process before producing a final answer. Instead of jumping directly to an answer, the model walks through intermediate steps. This dramatically improves performance on tasks requiring multi-step reasoning, math, logic, and complex analysis. Extended thinking in Claude and "thinking" in o-series OpenAI models are formal implementations of this principle.
Why it matters
Chain-of-thought is one of the most reliable techniques for getting better AI outputs with zero additional cost. Asking a model to "think step by step" before answering can improve accuracy by 20–40% on complex reasoning tasks. It is also invaluable for debugging — seeing the model's reasoning reveals where it goes wrong.
How it works
Simply add a phrase like "Let's think step by step," "think carefully before answering," or "show your reasoning" to your prompt. For Claude specifically, "use extended thinking" triggers the model's deliberative reasoning mode. The model then produces a structured chain of reasoning before its conclusion.
Examples in practice
Product prioritization
"Given these 5 feature requests and our current business priorities, which should we build first? Think step by step through each one." The model walks through each feature's impact, effort, and strategic alignment before recommending.