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Overview

In this lesson, you will learn:

• What Self-Consistency prompting is and why it improves accuracy
• What Tree-of-Thought (ToT) prompting is and how it helps explore multiple reasoning paths
• Beginner-friendly ways to implement these techniques
• Practical examples for math, logic, and multi-step reasoning tasks

By the end, you will know how to guide AI to generate more reliable and structured answers.

💡 Key Concepts

• Self-Consistency: Asking AI to reason multiple times independently and choosing the most consistent answer.
• Tree-of-Thought (ToT): Exploring multiple reasoning paths simultaneously before choosing a solution.
• Enhanced Accuracy: Both techniques reduce errors in complex problems.
• Stepwise Reasoning: Builds on Chain-of-Thought (CoT) by adding redundancy or multiple perspectives.

🧠 Concept Explanation

1. Self-Consistency Prompting

CoT gives step-by-step reasoning, but a single attempt may be wrong.

Self-Consistency solves this by:

1. Asking AI to generate multiple CoT reasoning paths independently
2. Selecting the most frequent or consistent answer

Example – Math Problem:

Problem: 23 × 47
Self-Consistency Approach:
- Run CoT reasoning 5 times
- Collect all final answers
- Choose the answer appearing most frequently

• Reduces errors compared to a single CoT attempt.

2. Tree-of-Thought (ToT) Prompting

ToT prompting explores multiple possible reasoning paths simultaneously before choosing a solution.

Why it helps:

• Useful for problems with multiple valid approaches
• AI evaluates branches of reasoning, picks optimal path
• Reduces mistakes and hallucinations

Example – Logic Puzzle:

Puzzle: John, Mary, and Sam are different heights. Who is tallest?
ToT Approach:
- Branch 1: John > Mary > Sam
- Branch 2: Mary > John > Sam
- Branch 3: Sam > John > Mary
Evaluate consistency in each branch
Pick branch that matches all known facts → John is tallest

3. Beginner-Friendly Strategies

1. Combine with CoT: Start with step-by-step reasoning before using self-consistency or ToT.
2. Multiple Outputs: Ask AI to generate 3–5 independent reasoning paths.
3. Select Consensus: Choose the most repeated answer or evaluate branches manually.
4. Use Roles: Assign AI a role to maintain style and perspective (e.g., “You are a detective solving the puzzle”).

🧩 Practical Examples

1. Self-Consistency Example (Math)

Prompt: "Solve 56 + 78 step by step. Provide 3 different reasoning paths."  
AI Output:
Path 1: 134
Path 2: 134
Path 3: 135
Most consistent answer: 134

2. Tree-of-Thought Example (Logic)

Prompt: "Determine who is tallest among Alice, Bob, and Carol. Alice > Bob, Bob > Carol. Show all reasoning paths."  
AI Output:  
- Path 1: Alice > Bob > Carol  
- Path 2: Bob > Alice > Carol  
- Path 3: Alice > Carol > Bob  
Evaluate paths → Path 1 is correct

3. Coding Problem with Multiple Approaches

Prompt: "You are a programming tutor. Generate 3 ways to reverse a string in Python and show steps for each."  
AI Output:  
- Method 1: Using slicing  
- Method 2: Using reversed() function  
- Method 3: Using a for loop  
Evaluate each method → All correct

⚙️ Tools for Beginners

• ChatGPT / OpenAI Playground: Generate multiple CoT outputs and compare results.
• Google Gemini / Bard: Test self-consistency by running multiple sessions for the same task.
• Notebooks / Scripts: Automate multiple runs and aggregate results.

🧭 Step-by-Step Beginner Activity

1. Pick a problem (math, logic, or coding).
2. Generate 1–2 CoT reasoning paths.
3. Generate 3–5 independent paths (self-consistency).
4. Compare results and choose the most frequent or consistent answer.
5. Try exploring different reasoning paths simultaneously (ToT) and evaluate.
6. Observe improvements over a single-step solution.

📝 Exercises

1. Solve 34 × 19 using self-consistency. Generate 3 reasoning paths.
2. Solve a logic puzzle using ToT, evaluating multiple branches.
3. Compare outputs: single CoT vs. self-consistency vs. ToT.
4. Experiment with assigning AI a role to guide reasoning in a puzzle or coding task.

🔍 Summary & Key Takeaways

• Self-Consistency: Multiple independent reasoning paths → choose the most consistent answer.
• Tree-of-Thought (ToT): Explore multiple reasoning branches → select optimal solution.
• Both techniques increase reliability and reduce errors in complex tasks.
• Beginners can combine CoT + Self-Consistency + ToT + Roles for powerful results.
• Mastering these methods is a critical step toward advanced prompt engineering in the Builder Zone.