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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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Chain of Thought (CoT)

Chain of Thought prompting guides models to reason step-by-step before answering. Instead of "What's the answer?", you prompt "Let's think step by step." This dramatically improves performance on math, logic, and commonsense reasoning. CoT reveals the reasoning process, making errors easier to debug and correct.

Interactive: Chain of Thought Demonstration

Watch how CoT breaks down a problem step-by-step:

Problem:
Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 balls. How many tennis balls does he have now?
Click "Show Step-by-Step Reasoning" to see Chain of Thought in action

How to Implement CoT

Zero-Shot CoT
"Question: [problem]\nLet's think step by step."
No examples needed. Just add "Let's think step by step."
Few-Shot CoT
Provide 2-3 examples with explicit reasoning steps. Model learns the pattern.
Best Results
GSM8K math: 65% → 92% accuracy. MultiArith: 17% → 78%. CSQA commonsense: 73% → 85%.
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When CoT Helps Most

Math word problems, multi-step reasoning, logical deduction, code generation with explanation. Less useful for: simple facts, classification, translation. Trade-off: 2-3x more tokens, but 20-30% accuracy gain on reasoning tasks. Use when correctness matters more than speed.

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