A prompting technique that instructs an LLM to show its step-by-step reasoning before answering. It 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.
Need help implementing this in your business?
Get Started