What is Chain of Thought?

Chain of Thought (CoT) is a prompting technique where you ask the AI to "think step-by-step" or "show your work" before answering. Instead of jumping to conclusions, the AI breaks down its reasoning into intermediate steps—like showing math work in school. This dramatically improves accuracy for logic, math, planning, and multi-step tasks.

When Should You Use This?

Use Chain of Thought when you need the AI to solve complex problems that require reasoning, calculation, or multi-step logic. Examples: math word problems, debugging code, planning projects, analyzing data, legal reasoning. Add "Let's think step-by-step" or "Show your reasoning" to your prompt. Best for GPT-4/Claude-level models—weaker models may not benefit as much.

Common Mistakes to Avoid

• •Using CoT for simple tasks—"What's 2+2? Think step-by-step" wastes tokens and time
• •Not giving the AI space to think—rushing with "be concise" defeats the purpose
• •Expecting it to work on weak models—small/fast models often can't chain reasoning well
• •Ignoring the reasoning—if the steps are wrong, the answer is wrong (check the work!)
• •Over-relying on it—CoT increases cost/latency, use only when accuracy matters more than speed

Real-World Examples

• •OpenAI o1—entire model built around chain-of-thought reasoning for complex problems
• •Math tutors—ask GPT to solve problems step-by-step so students see the process
• •Code debugging—"Walk me through this bug step-by-step" finds root causes faster
• •Financial analysis—"Analyze this P&L line-by-line" catches errors AI would miss if rushing