Embedding

Embeddings are among the most widely used yet often underexplained concepts in modern AI. Put simply, an embedding is a powerful way of representing the meaning of an entity in vector space. The goal is not merely to turn data into numbers, but to place semantically similar examples close together and unrelated ones farther apart. Semantic search, recommendation systems, RAG architectures, clustering, and document similarity all depend heavily on embedding quality. In other words, embeddings are not just a technical detail; they are a strategic layer that directly affects product experience, search quality, and information access performance.