Large Language Models: A–Z

Large Language Models (LLMs) like GPT, Claude, and Gemini are built on a deceptively simple premise: predict the next word. But behind that simplicity lies a mountain of computation, mathematics, and statistical learning. Understanding how LLMs function requires pulling back the curtain on tokenization, embeddings, and the now-legendary Transformer architecture.

LLMs begin by breaking down text into discrete units called *tokens*, fragments of words or characters. These tokens are then mapped to high-dimensional vectors known as *embeddings*. These embeddings allow the model to represent language in a numerical space, where the distance and direction between vectors encode relationships: 'king' - 'man' + 'woman' ≈ 'queen' is a famous example.

The Math of Meaning

At the core of every LLM is the Transformer, a model architecture introduced in 2017 that uses *self-attention* to determine the contextual weight of each token in a sequence. Instead of reading left to right, Transformers process all tokens at once, using attention matrices to identify which words influence each other most. These matrices are essentially massive weighted dot products, tuned through backpropagation over billions of training samples.

Scale, Generalization, and Limits

The power of an LLM comes from its scale. It is trained on terabytes of text across the web, books, code, and more. Through gradient descent and optimization across billions of parameters, the model begins to 'learn' latent structures of human language. But this learning is statistical, not semantic. LLMs don’t 'understand' meaning in a human sense. They pattern-match it with staggering fluency.

The result is an engine of text generation, capable of code, poetry, analysis, and conversation. But building these models comes at a cost: massive compute, careful alignment, and ongoing research into bias, hallucination, and ethical deployment. LLMs are not just tools. They’re systems. And understanding how they work is the first step to using them responsibly and effectively.