What are embeddings in the context of LLMs?

Embeddings are dense numeric vectors that represent text in a way that preserves semantic meaning. Texts with similar meanings produce vectors that are close in the high-dimensional embedding space. Embeddings are the foundation of semantic search, retrieval-augmented generation, clustering, classification, and recommendation systems.