This episode explains the attention mechanism in Transformer architecture, a crucial component of large language models (LLMs). It breaks down the process into key steps: creating and updating word embeddings to reflect contextual meaning, and attention scores.

The explanation uses analogies and illustrations to clarify complex concepts. This episode also covers the encoder-decoder structure of Transformers and its variations.