This episode explores in-context learning, the idea that you can dramatically change how a model behaves just by showing it examples inside the prompt, without changing a single weight. It walks through zero-shot, one-shot, and few-shot prompting, when each one tends to work best, and why examples shape not just the answer but also the format, tone, and structure of the response. It also gets into some of the more surprising research around this, including how models can still perform well even when example labels are wrong, why example order can materially affect accuracy, and why one strong example can sometimes outperform several mediocre ones. The episode closes by framing few-shot prompting as one of the most practical and powerful skills in prompt engineering, while also pointing to the limits of prompting when a task becomes too complex.