Understanding Large Language Models (LLMs) requires some high-school level mathematics, particularly in vectors and high-dimensional spaces. The article explains how vectors represent likelihoods for tokens and discusses concepts like vocab space, embeddings, and the dot product, which are essential for grasping how LLMs function and compare meanings within their vector spaces.