chain-of-thought prompting

Chain-of-thought (CoT) prompting elicits step-by-step reasoning before final answers, dramatically improving accuracy. **Mechanism**: Ask model to "think step by step" or demonstrate reasoning in examples. Model generates intermediate steps that guide toward correct answer. **Implementation**: Zero-shot ("Let's think step by step"), few-shot (examples showing reasoning), or structured templates. **Why it works**: Breaks complex problems into manageable steps, reduces reasoning errors, leverages model's training on step-by-step explanations. **Best for**: Math problems, logic puzzles, multi-hop reasoning, complex analysis, code debugging. **Limitations**: Longer outputs (cost/latency), can generate plausible but wrong reasoning, small models may not benefit. **Variants**: Self-consistency (multiple paths, vote on answer), Tree of Thoughts (explore branches), least-to-most (decompose then solve). **Emergent ability**: Works best in large models (100B+ parameters), limited effect in smaller models. **Best practices**: Be explicit about step-by-step format, verify reasoning not just answers, combine with self-consistency for important tasks. One of the most practical prompt engineering techniques.