Chain of Thought
Chain of thought is a prompting technique where you ask AI to reason through a problem step-by-step before providing an answer. By making the AI's reasoning explicit, you get more accurate results for complex tasks like debugging, architecture decisions, and multi-step implementations.
Origin
Introduced in the 2022 paper 'Chain-of-Thought Prompting Elicits Reasoning in Large Language Models' by Google researchers, demonstrating significant improvements in AI reasoning capabilities.
Example
Chain of thought prompting transforms how AI handles complex problems. Rather than jumping straight to an answer (which often leads to errors), the AI reasons through each step — and you can follow along to verify its logic.
Why It Works
Large language models are better at generating correct answers when they "show their work." This happens because:
- Each reasoning step builds context for the next
- Errors become visible and correctable mid-process
- Complex problems get broken into manageable pieces
How to Trigger Chain of Thought
Simple additions to your prompts:
- "Think through this step by step"
- "Explain your reasoning as you go"
- "Before answering, analyze the problem"
- "Walk me through your thought process"
When to Use It
Chain of thought is especially valuable for:
- Debugging — Understanding why code fails
- Architecture decisions — Weighing trade-offs
- Complex implementations — Multi-file changes
- Code review — Analyzing existing code
For simple tasks like generating a basic function, chain of thought adds unnecessary overhead. Save it for problems where reasoning matters.
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