Embeddings turn unstructured content into numerical vectors. Two pieces of text with similar meaning produce vectors that are close in vector space, which lets you search by meaning instead of keyword match.
Embeddings are the backbone of semantic search, RAG retrieval, recommendation, and clustering. OpenAI text-embedding-3-large is the popular default in 2026. Cohere, Voyage, and open-source alternatives like BGE-M3 are competitive. Embedding quality matters more than people expect - swapping embeddings can move RAG accuracy by 10-20 points.
