A prompting technique that instructs an LLM to show its step-by-step reasoning before answering — dramatically improves accuracy on multi-step problems by giving the model space to think.
Chain-of-Thought prompting adds an instruction like 'Let's think step by step' or shows few-shot examples that demonstrate reasoning before the answer. The model then generates intermediate reasoning steps, which condition the final answer. CoT works because LLM outputs are autoregressive — earlier tokens influence later ones, so giving the model space to 'reason out loud' produces better final answers. Modern models (GPT-4, Claude, Gemini) often default to CoT-style internal reasoning even without explicit prompting.
Improving a financial calculation prompt from 70% accuracy to 95% by adding 'First, list the input numbers. Then identify the operation. Then compute step by step.'
Chain-of-Thought is the simplest, most reliable prompt-engineering technique to improve LLM accuracy on reasoning tasks — often turning unusable outputs into production-grade ones.
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