An embedding is a list of numbers that represents the meaning of a word, sentence or image, placed so that similar things sit close together. This lets a computer measure how related two items are, which underpins semantic search, recommendations and many AI features.
Because embeddings capture meaning rather than exact wording, a search can match a question to a differently worded answer, and a service can suggest items that feel similar in spirit. The quality depends on the model that creates them, and embeddings can carry biases from their training data, so they are a powerful but not neutral building block that teams test with care.