The Full Transformer — Encoder + Decoder

#The Problem

A single attention layer is a feature extractor, not a model. One matmul per layer is not enough capacity for language. You need depth — and depth breaks without the right plumbing.

The 2017 Vaswani paper packaged six design decisions that turned one attention layer into a stackable block. Every transformer since — encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5) — inherits the same skeleton. In 2026 the blocks have been refined (RMSNorm, SwiGLU, pre-norm, RoPE) but the skeleton is identical.

This lesson is the skeleton. Next lessons specialize it — 06 for encoders, 07 for decoders, 08 for encoder-decoder.