A numerical vector representation of text, images, or other data that captures semantic meaning in a format suitable for machine learning operations. Embeddings allow AI systems to compare meaning rather than exact wording. They are fundamental to semantic search, retrieval, and similarity matching in modern AI architectures. For more on how these work in practice, see our systems analysis of RAG architecture.
Embeddings enable semantic search and similarity comparisons, forming the foundation of modern retrieval-augmented generation systems.
AI retrieval systems convert queries and documents to embeddings, then use vector similarity to find relevant content for response generation.
Embeddings enable AI systems to find semantically similar content even when exact keywords differ.