The Transformer architecture, introduced in the 2017 paper “Attention Is All You Need,” underlies virtually every major modern AI language model — worth understanding at least at a conceptual level, layer by layer.
The Core Innovation: Self-Attention
Before Transformers, sequence models (RNNs, LSTMs) processed text token by token, sequentially — genuinely slow to train and prone to “forgetting” earlier context in long sequences. Self-attention instead lets every token look directly at every other token in the sequence simultaneously, computing how much each token should “attend to” every other one — both faster to train (parallelizable) and better at capturing long-range dependencies.
The Layer Structure
Each Transformer block contains:
1. Multi-head self-attention - tokens attend to each other from multiple
different "perspectives" simultaneously
2. Add & normalize - residual connection + layer normalization,
helps deep networks train stably
3. Feed-forward network - processes each token's representation independently
4. Add & normalize (again)
Modern LLMs stack DOZENS of these blocks on top of each other
Why “Multi-Head”
A single attention mechanism might learn to focus on, say, grammatical relationships — multiple attention “heads” running in parallel let the model simultaneously capture different kinds of relationships (grammar, meaning, coreference) in the same layer, then combine those different perspectives together.
Encoder vs Decoder
The original Transformer had both an encoder (processes input) and decoder (generates output) — most modern LLMs (GPT-style) use a decoder-only variant, generating text one token at a time, each new token attending back to everything generated so far.