Since the seminal 2017 paper "Attention Is All You Need," the Transformer architecture has dominated AI. But how does it actually work? This guide breaks down keys, queries, and values in plain English.

Imagine being at a loud party. You can focus on one conversation while tuning out others. This is "Attention." The Transformer mechanism allows the model to focus on relevant words in a sentence, regardless of how far apart they are.

We visualize the flow of information through the encoder and decoder stacks, explaining why this parallelization capability allowed GPT models to scale so effectively compared to previous RNNs.