An embedding is a dense vector representation of a discrete input (word, sentence, image, user) in a continuous space where geometric distance reflects semantic similarity. Word2Vec and GloVe popularized the idea for words; modern transformer models produce contextual embeddings that change based on surrounding text. Embeddings are the bridge between symbolic inputs and the numerical operations neural networks perform, and they power similarity search, retrieval-augmented generation, and recommendation systems.