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Chain-of-Thought(CoT)
Breaks down complex problems into step-by-step intermediate reasoning steps
In 30 seconds
- What
- Prompts the model to show intermediate reasoning steps before reaching a final answer, making the solution path explicit.
- When to use
- Problems requiring multi-step logic, math, or causal reasoning where showing work helps catch errors and improves accuracy.
- Watch out
- Longer reasoning can amplify early mistakes; each step compounds errors from prior steps, degrading final answers.
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Video Explanation
Chain-of-Thought: Overview
Breaks down complex problems into step-by-step intermediate reasoning steps
- Decomposes complex problems into manageable sub-problems
- Provides transparent reasoning process
- Uses "think step by step" approach
- Foundation for advanced agent actions
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References
The papers, specifications, and repositories this pattern is based on.
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)arXiv:2201.11903
- Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)arXiv:2203.11171
- Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)arXiv:2205.10625
- Large Language Models are Zero-Shot Reasoners (Kojima et al., 2022)arXiv:2205.11916
- Anthropic Claude Chain of Thought Best Practices
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