From Words to Intelligence: How LLMs Actually Work (Without the Math Headache)

Large Language Models often feel magical. You type a sentence, and suddenly an AI writes code, explains physics, or drafts emails. But under the hood, the system is doing something surprisingly structured. Let’s walk through the core building blocks of modern AI models in a simple and fun way. 1. Tokenization — Breaking Language into Pieces Before an AI understands text, it must split the sentence into smaller units called tokens . Example sentence: "I love artificial intelligence" Tokenized form might look like: ["I", "love", "artificial", "intelligence"] Sometimes tokens are even smaller: ["art", "ificial", "intelli", "gence"] This depends on the model’s vocabulary size . Vocabulary Size This is the number of tokens a model knows. Example: Model Approx Vocabulary Small models ~30k tokens Modern LLMs 100k+ tokens Think of it like a dictionary the AI uses to read text . 2. Vectors — Turning Words into Numbers Computers don’t understand words. They understand numbers . So each token bec