Chain-of-thought prompting was shown to significantly improve LLM performance on reasoning tasks. By asking 'think step by step' or providing examples with explicit reasoning, models produce more accurate answers — especially for math, logic, and multi-step problems.
Variants include zero-shot CoT ('think step by step'), few-shot CoT (providing example reasoning chains), and tree-of-thought (exploring multiple reasoning paths).