Chain-of-Thought Builder
Create step-by-step reasoning prompts to improve AI problem-solving accuracy
Example Templates
Problem Statement
Reasoning Steps
Final Answer
Output Style
Generated Chain-of-Thought
Let's solve this step by step. **Problem:** What is 15% of 240? **Reasoning:** 1. Identify the percentage we need to find (15%) 2. Convert the percentage to decimal: 15% = 0.15 3. Multiply the decimal by the number: 0.15 × 240 4. Calculate: 0.15 × 240 = 36 **Answer:** 15% of 240 is 36.
System Prompt Instruction (for enabling CoT)
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What is Chain-of-Thought Prompting?
Chain-of-Thought (CoT) prompting is a technique that dramatically improves AI accuracy on complex reasoning tasks by encouraging models to "show their work." Instead of jumping straight to an answer, CoT prompts guide the AI to break problems into intermediate steps, explain its reasoning at each stage, and only then provide a final answer.
Research from Google and other labs has shown that CoT prompting can improve accuracy on math problems by 50% or more, and similar gains have been observed on logic puzzles, multi-step questions, and complex analysis tasks. It's one of the most effective prompting techniques available today.
Why Chain-of-Thought Works
Reduces Reasoning Errors
By breaking complex problems into smaller steps, the AI is less likely to make logical leaps or skip important considerations. Each step acts as a checkpoint.
Activates Relevant Knowledge
Intermediate steps help the model access and apply the right knowledge at each stage, rather than trying to solve everything at once.
Enables Self-Correction
When the AI shows its work, it can catch its own mistakes. Explicit reasoning makes errors visible and correctable.
Improves Transparency
Users can verify the logic and understand how the AI reached its conclusion, building trust and enabling feedback.
How to Use This Tool
- Start with a template — Choose from Math, Logic, Code Debugging, or Decision Making examples to see the pattern.
- Define the problem — Clearly state the question or task that needs to be solved.
- Break down reasoning steps — Add individual steps that move from problem to solution. Each step should make one logical advancement.
- Add or reorder steps — Use the controls to add new steps, move them up/down, or remove unnecessary ones.
- State the final answer — Provide a clear, direct conclusion based on the reasoning.
- Choose output style — Select numbered, bullet, or narrative format based on your preference.
- Copy and use — Use the generated CoT as a few-shot example, or copy the system instruction to enable CoT in all responses.
When to Use Chain-of-Thought
✓ Great For
- • Multi-step math problems
- • Logical reasoning and syllogisms
- • Code debugging and analysis
- • Complex decision making
- • Strategy and planning tasks
- • Root cause analysis
✗ Not Needed For
- • Simple factual questions
- • Creative writing (can stifle creativity)
- • Quick conversational responses
- • Tasks requiring intuition over logic
- • Single-step calculations
CoT Prompting Techniques
Zero-Shot CoT
Simply add "Let's think step by step" to your prompt. This phrase alone can trigger reasoning behavior in many models without providing any examples.
Few-Shot CoT
Provide 2-3 examples of problems solved with step-by-step reasoning. The AI learns the pattern and applies it to new problems. This tool helps you create these examples.
Self-Consistency
Generate multiple CoT reasoning paths and pick the most common answer. This reduces errors by averaging out reasoning mistakes.
Tree-of-Thought
An advanced variant where the AI explores multiple reasoning branches before selecting the best path. More complex but highly effective.
Tips for Effective Chain-of-Thought
- One step, one operation — Each reasoning step should perform exactly one logical operation or calculation.
- Include intermediate results — Show calculations and conclusions at each step so errors can be caught.
- Use the trigger phrase — "Let's think step by step" or "Let's solve this carefully" activates reasoning mode.
- End with a clear answer — Always conclude with an explicit, direct answer to the original question.
- Verify the logic — Review the chain to ensure each step follows logically from the previous one.
Frequently Asked Questions
Does CoT work with all AI models?
CoT is most effective with larger models (GPT-4, Claude, Gemini). Smaller models may not benefit as much because they lack the capacity for complex reasoning. Always test with your target model.
How many steps should my chain have?
There's no fixed number — use as many steps as the problem logically requires. Simple problems might need 2-3 steps; complex ones might need 8-10. The key is that each step should be meaningful.
Can I use CoT for creative tasks?
Generally not recommended. CoT works best for logical, analytical tasks. For creative writing, the structured reasoning can make outputs feel mechanical. Use it sparingly for planning story structure, not for generating prose.
Does CoT increase token usage?
Yes, CoT responses are longer because they include reasoning. This increases costs but usually provides much better accuracy for complex tasks. Consider the trade-off based on your use case.
Related Tools
- • Few-Shot Builder — Create multiple CoT examples for few-shot learning
- • System Prompt Builder — Add CoT instructions to your system prompts
- • Prompt Diff Viewer — Compare CoT versions to optimize performance