Shay breaks down the encoder vs decoder split in transformers: encoders (BERT) read the full text with bidirectional attention to understand meaning, while decoders (GPT) generate text one token at a time using causal attention. She ties the architecture to training (masked-word prediction vs next-token prediction), explains why decoder-only models dominate today (they can both interpret prompts and generate efficiently with KV caching), and previews the next episode on the MLP layer, where most learned knowledge lives.