Chain of Thought (CoT) prompting asks the AI to show its reasoning step-by-step before giving a final answer. This technique dramatically improves accuracy on math, logic, analysis, and complex reasoning tasks.
Break the problem into explicit steps the AI should follow
Ask the AI to 'think step by step' or 'show your reasoning'
The AI's intermediate reasoning steps lead to more accurate final answers
Works especially well for math, logic, coding, and multi-step analysis
Template
Example Output
Template
Example Output
Template
Example Output
Adding 'Let's think step by step' to any prompt improves accuracy on complex tasks
For math problems, always ask AI to 'show your work' and 'verify the answer'
Provide the specific steps you want followed for domain-specific analysis
Chain of Thought is most valuable for tasks where the AI would otherwise rush to an answer
Combine with 'Before answering, consider what could go wrong' for even better results
When AI models show their reasoning, each step acts as context for the next step. This prevents the model from jumping to incorrect conclusions. Research shows CoT prompting improves accuracy by 20-40% on complex reasoning tasks.
Use it for: math and calculations, multi-step analysis, debugging code, strategic decisions, and any task where the answer depends on multiple intermediate reasoning steps. For simple factual questions, CoT is unnecessary overhead.
CoT works best with larger, more capable models (GPT-4, Claude 3, Gemini Pro). Smaller models may not benefit as much. The technique is model-agnostic — it works with any AI that can follow step-by-step instructions.
Take our free AI course and learn techniques that go beyond templates.
Start Free AI Course →