🤖 AI EngineeringID:
chain-of-thought-promptingChain-of-Thought Prompting
CoT 思维链与分步推导
🎯Core Definition
Chain-of-Thought (CoT) Prompting is a paradigm that elicits intermediate reasoning steps from an LLM before producing the final answer; variants include Zero-shot CoT (appending `Let's think step by step`), Few-shot CoT (supplying step-by-step human derivations in exemplars), Least-to-Most Prompting (solving sub-problems iteratively), and Auto-CoT; it allocates dynamic autoregressive computation budget across Transformer layers to compute and store intermediate state representations.
💡Use Cases
Multi-step mathematical reasoning (GSM8K, MATH), complex algorithmic code generation, enterprise rule evaluation, and causal deduction.
⚡Key Problems Solved
Direct answering forces the model to jump directly to final predictions within a single forward pass, collapsing on complex multi-variable reasoning; CoT breaks reasoning into sequential verbalized tokens, slashing cognitive leap errors.
🎯5 High-Frequency Exam Points
1
From an autoregressive Transformer attention standpoint, why does generating intermediate CoT tokens increase test-time compute capacity?
2
How does Self-Consistency improve accuracy by sampling diverse reasoning chains and taking the majority vote on final outputs?
3
How does Process Supervision (PRMs) validate intermediate CoT steps against unfaithful rationalization errors?
4
What are the advantages of Least-to-Most Prompting over standard CoT when addressing 5+ step complex dependencies?
5
How to apply XML tag delimiters (`<thought>` / `<answer>`) to structure and isolate intermediate reasoning from user-facing responses?
📖 In-depth Guide:📄 prompt-engineering-and-guardrails →
Updated 2026-08-14🎯
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