Transformer Architecture Introduction — The Model Behind Modern LLMs

Why it matters

You can use LLMs without knowing transformers, but you can't reason about their behavior. Understanding the architecture is what turns 'magic' into engineering.

The architecture

Input: token IDs (from tokenization). Embed to vectors.

N transformer blocks: attention + feedforward, with residual and normalization.

Output: unembed to token probabilities.

How it works end to end

Attention: each token gathers information from every other token via query-key-value dot products.

Feedforward: per-token MLP that transforms attention output.

Residual: outputs added to inputs, enabling deep networks.

Layer norm: normalizes across features for stability.

Autoregressive: generates one token at a time by sampling from output distribution.