How Do Vector Embeddings Work? (Turning Words Into Meaning) — [AI Stack 03]

A vector embedding is a list of numbers that captures the meaning of a piece of text as a point in space, so that things with similar meaning sit close together and a computer can measure how related they are. Episode 3 of The AI Stack explains how embeddings turn words into meaning a machine can compare, the famous "king minus man plus woman equals queen" result, how similarity is actually measured, and why embeddings are the foundation under semantic search, recommendations, and RAG. We name the real embedding models from OpenAI, Cohere, Voyage, and the open-source sentence-transformers. ▶ The AI Stack — full course in order: [playlist] ← Previous: [AI Stack 02] How Tokenization Works → Next: [AI Stack 04] What a Context Window Is #embeddings #vectorsearch #LLM