A numerical representation of text (or audio, or images) in a high-dimensional vector space, such that semantically similar inputs end up close together in the space. The sentence "let's reschedule" and "can we move this?" have very different surface words but produce embeddings that are close neighbors. Embeddings power semantic search in meeting copilots: instead of asking users to remember the exact words they used in a meeting, you embed both the query and every transcript chunk, then return the chunks whose embeddings are closest to the query's. Embeddings are also the substrate of RAG — the retrieval step is typically a vector-similarity search over an embedding index. Modern embeddings (1024 to 3072 dimensions) capture nuance well enough to distinguish related-but-distinct concepts.