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Advanced Reasoning Techniques

Master advanced reasoning techniques for AI agents including chain-of-thought, tree of thoughts, and self-consistency

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Introduction

Basic prompting ("Answer this question") often fails for complex reasoning tasks. Advanced reasoning techniques— Chain of Thought (CoT), Tree of Thoughts (ToT), Self-Consistency—dramatically improve accuracy on math, logic, and multi-step problems. These techniques guide models to think step-by-step, explore alternatives, and verify answers.

Why Advanced Reasoning Matters

GPT-4 with standard prompting: 65% accuracy on GSM8K math benchmark. With Chain of Thought: 92% accuracy. Tree of Thoughts on Game of 24: 74% success vs 4% with standard prompting. Self-Consistency reduces error rates by 30-50%. Advanced reasoning unlocks capabilities hidden in base models.

Interactive: Reasoning Techniques Explorer

Explore four major advanced reasoning techniques:

Reasoning Technique Progression

1.
Standard Prompting
Direct question → direct answer. Fast but fails on complex tasks.
2.
Chain of Thought
Step-by-step reasoning. 20-30% accuracy improvement on reasoning tasks.
3.
Tree of Thoughts
Explore multiple paths, backtrack if needed. Best for strategic planning.
4.
Self-Consistency
Generate multiple reasoning paths, vote on answers. Highest accuracy.
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When to Use Advanced Reasoning

Not every task needs advanced reasoning. Use for: math problems, logical puzzles, multi-step planning, code generation, complex decisions. Skip for: simple lookups, classification, translation. Trade-off: Higher accuracy but 3-10x more tokens and latency. Choose wisely based on task complexity.