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🔗
Chain-of-Thought(CoT)
Breaks down complex problems into step-by-step intermediate reasoning steps
Complexity: lowReasoning Techniques
Video Explanation
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🎯 30-Second Overview
Pattern: Generate explicit step-by-step reasoning before final answer
Why: Makes reasoning transparent, improves accuracy on complex problems, enables error detection
Key Insight: Add 'Let's think step by step' trigger + numbered reasoning steps → 2-5x accuracy gain
⚡ Quick Implementation
1Prompt Setup:Add "Let's think step by step" trigger
2Examples:Provide 2-3 reasoning examples
3Structure:Number each reasoning step clearly
4Verify:Check logical flow & calculations
5Conclude:State final answer explicitly
Example: Problem → Step 1: [reasoning] → Step 2: [reasoning] → Answer: [result]
📋 Do's & Don'ts
✅Use consistent step numbering (Step 1, Step 2, etc.)
✅Include verification steps for calculations
✅Provide diverse few-shot examples
✅Break complex problems into sub-problems
✅Use clear reasoning triggers ("Let's think...")
❌Skip intermediate steps for "obvious" calculations
❌Use CoT for simple, direct questions
❌Ignore token cost increase (2-5x overhead)
❌Make reasoning steps too verbose or redundant
❌Forget to validate logical consistency
🚦 When to Use
Use When
- • Complex mathematical problems
- • Multi-step logical reasoning
- • Decision-making with trade-offs
- • Educational/explainable AI
- • When accuracy > speed
Avoid When
- • Simple factual questions
- • Real-time/low-latency needs
- • Creative/subjective tasks
- • Tight token budgets
- • When implicit reasoning works
📊 Key Metrics
Reasoning Accuracy
Correct final answers vs baseline
Step Correctness
Valid intermediate reasoning steps
Token Efficiency
Accuracy gain per token increase
Interpretability
Human rating of reasoning clarity
Consistency Rate
Agreement across multiple chains
Error Attribution
Ability to identify wrong steps
💡 Top Use Cases
Math Word Problems: "If a train travels 60 mph for 2 hours..." → step-by-step calculation
Logic Puzzles: Multi-conditional problems requiring systematic elimination
Code Debugging: Trace execution path step-by-step to find errors
Financial Analysis: Break down ROI calculations with intermediate steps
Medical Diagnosis: Systematic symptom analysis → differential diagnosis
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🔗
Chain-of-Thought(CoT)
Breaks down complex problems into step-by-step intermediate reasoning steps
Complexity: lowReasoning Techniques
Video Explanation
Loading video explanation...
🎯 30-Second Overview
Pattern: Generate explicit step-by-step reasoning before final answer
Why: Makes reasoning transparent, improves accuracy on complex problems, enables error detection
Key Insight: Add 'Let's think step by step' trigger + numbered reasoning steps → 2-5x accuracy gain
⚡ Quick Implementation
1Prompt Setup:Add "Let's think step by step" trigger
2Examples:Provide 2-3 reasoning examples
3Structure:Number each reasoning step clearly
4Verify:Check logical flow & calculations
5Conclude:State final answer explicitly
Example: Problem → Step 1: [reasoning] → Step 2: [reasoning] → Answer: [result]
📋 Do's & Don'ts
✅Use consistent step numbering (Step 1, Step 2, etc.)
✅Include verification steps for calculations
✅Provide diverse few-shot examples
✅Break complex problems into sub-problems
✅Use clear reasoning triggers ("Let's think...")
❌Skip intermediate steps for "obvious" calculations
❌Use CoT for simple, direct questions
❌Ignore token cost increase (2-5x overhead)
❌Make reasoning steps too verbose or redundant
❌Forget to validate logical consistency
🚦 When to Use
Use When
- • Complex mathematical problems
- • Multi-step logical reasoning
- • Decision-making with trade-offs
- • Educational/explainable AI
- • When accuracy > speed
Avoid When
- • Simple factual questions
- • Real-time/low-latency needs
- • Creative/subjective tasks
- • Tight token budgets
- • When implicit reasoning works
📊 Key Metrics
Reasoning Accuracy
Correct final answers vs baseline
Step Correctness
Valid intermediate reasoning steps
Token Efficiency
Accuracy gain per token increase
Interpretability
Human rating of reasoning clarity
Consistency Rate
Agreement across multiple chains
Error Attribution
Ability to identify wrong steps
💡 Top Use Cases
Math Word Problems: "If a train travels 60 mph for 2 hours..." → step-by-step calculation
Logic Puzzles: Multi-conditional problems requiring systematic elimination
Code Debugging: Trace execution path step-by-step to find errors
Financial Analysis: Break down ROI calculations with intermediate steps
Medical Diagnosis: Systematic symptom analysis → differential diagnosis
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.