Introduction
In 2017, a Google team published a paper with a bold title: "Attention Is All You Need" (Vaswani et al.). They threw away recurrence and convolutions entirely and built a translation model from just attention, feed-forward layers, residual connections and normalization. It trained much faster than RNN models (because everything runs in parallel) and translated better.
That architecture, the transformer, became the foundation of modern AI: BERT, GPT, T5, vision transformers, speech models, protein-structure models and multimodal assistants are all built from its blocks.
Think of a transformer as a factory with identical workstations in a row. At each station (a block), every token first talks to the other tokens (attention) to gather context, then thinks on its own (a feed-forward network) to process what it gathered. Each station adds its improvements on top of the previous version (residual connections), and a quality check keeps the numbers in a steady range (layer normalization). After a dozen stations, each token's vector deeply reflects its meaning in context.