In this episode, Shay walks through the transformer's attention mechanism in plain terms: how token embeddings are projected into queries, keys, and values; how dot products measure similarity; why scaling and softmax produce stable weights; and how weighted sums create context-enriched token vectors.

The episode previews multi-head attention (multiple perspectives in parallel) and ends with a short encouragement to take a small step toward your goals.