These slides reveal what actually happens before an LLM predicts a single token: every word must be converted into numbers, and that conversion – an embedding – maps language into a high-dimensional vector space where meaning becomes geometry and similar words occupy similar positions.

Each slide builds toward one key insight: embeddings are not an add-on, they are the foundation of how LLMs represent language – encoding context, syntax, semantics, and usage patterns, and powering everything from semantic search and RAG to vector databases and AI agent reasoning.

For a full definition and a breakdown of how them work, check our glossary article on embeddings.