AI Fundamentals

Multimodal Embedding

A vector representation of an input (text, image, audio, video) in a shared embedding space, enabling cross-modal search — e.g., finding images relevant to a text query, or vice versa. Powers modern multimodal RAG and semantic search across mixed content types.

Related terms

Embedding
A numeric vector representation of text (or images, audio, etc.) produced by a model such that semantically similar inputs map to nearby vectors. Used to power search, clustering, and retrieval in modern GenAI stacks.
Vector Database
A database optimized for storing and querying high-dimensional vector embeddings via approximate nearest-neighbor search. Common choices include Pinecone, Weaviate, Qdrant, Milvus, and pgvector. Central to most RAG architectures.

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Where this fits

Multimodal Embedding is part of the AI Fundamentals vocabulary used in the Generative AI Maturity Framework. See the full glossary for the complete set of 149 defined terms, or take the free maturity assessment to see where your organisation stands.