Architecture.

A from-scratch tour of what's actually inside an LLM: how a transformer turns tokens into predictions, what Query, Key, and Value really mean, and how generating text one token at a time builds the KV cache — the growing pool of memory that makes inference so expensive.

A neural network never sees a word, an image, or a sound — only a list of numbers. Starting from that one fact and a single neuron, this guide builds the whole machine: how any input becomes numbers, why weights, biases, and activations each exist, and how neurons stack into layers and layers into a model.