Chain-of-Thought

Chain-of-Thought (CoT) is a prompting technique that encourages language models to break down complex problems into intermediate reasoning steps before providing a final answer. By explicitly showing the thinking process, models often achieve significantly better performance on tasks requiring logic, math, or multi-step reasoning.

CoT can be implemented through explicit instructions ("Think step by step"), by providing examples that demonstrate reasoning patterns, or through zero-shot CoT prompting with phrases like "Let's think about this carefully." Variants include Tree-of-Thought (exploring multiple reasoning paths) and Self-Consistency (sampling multiple CoT chains and selecting the most common answer).

For AI engineers, CoT is valuable for: improving accuracy on complex queries, debugging model reasoning, creating more interpretable outputs, and building reliable AI systems that handle nuanced problems. The technique is now standard practice for any application involving reasoning, analysis, or problem-solving.