Chain-of-Thought (CoT) reasoning is a prompt engineering technique that transforms how large language models solve complex problems by explicitly requesting intermediate reasoning steps before generating final answers. Introduced by Google Research in 2022, CoT dramatically improves LLM performance on multi-step reasoning tasks—often by 30-60+ percentage points—by mimicking human problem-solving: breaking down questions, showing work, and building toward solutions iteratively. Unlike direct-answer prompting, CoT makes the model's reasoning process visible and verifiable, enabling better accuracy on mathematical, logical, and symbolic tasks while providing interpretability for debugging and trust. The key insight: explicitly modeling reasoning chains unlocks capabilities that remain dormant in standard prompting.