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Chain-of-Thought (CoT) & Step-by-Step Reasoning
Unlocking Deep Problem Solving
When humans solve complex logic puzzles or math problems, we write down intermediate steps. LLMs work better when forced to do the exact same thing!
What is Chain-of-Thought (CoT)?
Chain-of-Thought prompting asks the LLM to break down its reasoning into intermediate steps before giving a final answer.
The Magic Trigger Phrase: Adding "Let's think step by step" to a prompt forces the model to generate intermediate reasoning tokens, boosting accuracy on complex tasks by up to 50%!
Example Comparison
❌ Direct Prompting: > "A store has 15 apples. They sell 4 on Monday and double their stock on Tuesday. How many apples do they have?" > AI might jump to an incorrect mental math estimate.
✅ Chain-of-Thought Prompting: > "Solve this problem step by step: > 1. Start with initial count. > 2. Subtract Monday sales. > 3. Double remaining count for Tuesday. > 4. State the final total."*
Self-Consistency Technique
For critical calculations or logic puzzles, ask the AI to generate 3 independent reasoning chains and take the majority consensus answer!