Chain-of-Thought Reasoning involves generating intermediate reasoning steps when solving complex problems, rather than jumping directly to an answer. It can improve interpretability by revealing how a model arrives at its conclusion. Recent large language models often benefit from prompts that encourage chain-of-thought explanations.