Knowledge distillation is a technique where a large, powerful «teacher» model trains a smaller «student» model to imitate its behavior, transferring most of the capability at a fraction of the size and cost. It is like a master artisan in Fès training an apprentice: the apprentice never sees every piece the master ever made, but learns the craft by observing the master's work. Distilled models run cheaper and faster, making AI viable on modest servers or mobile phones.