This episode covers chain of thought prompting, how asking a model to show its reasoning makes it measurably better at complex tasks, and why that works at a mechanical level. It walks through manual and zero-shot chain of thought, then three advanced extensions: self-consistency, Tree of Thought, and step-back prompting. It closes with when chain of thought actually helps versus when it just adds overhead.