Embeddings are numerical representations (vectors) of text, images, or audio that capture underlying semantic meaning, enabling AI systems to process and compar
An embedding model maps text (or images, audio, code) to a vector, a list of hundreds to thousands of numbers, positioned so semantically similar content lands close together in the vector space. Similarity is computed geometrically (cosine similarity), which turns 'find related meaning' into fast math. Pipelines embed documents at index time and queries at search time, then retrieve nearest neighbors from a vector database.
Embeddings are the connective tissue of modern AI: they power semantic search, RAG retrieval, recommendation, clustering, deduplication, and memory for agents. Retrieval quality, and therefore most enterprise AI quality, is bounded by how well the chosen embedding model represents your domain.
Benchmark on your own data: build a small set of queries with known-correct documents and measure retrieval accuracy across candidate models. Public leaderboards (like MTEB) shortlist candidates, but domain fit decides: legal, medical, and code text often need specialized or fine-tuned embedders.
The number of values per vector (e.g. 768 or 3072). Higher dimensions can capture more nuance but cost more storage and compute; many 2026 models support shortening vectors (Matryoshka embeddings) to trade accuracy for efficiency per use case.
Yes: embeddings are snapshots of content. Production systems run incremental pipelines that re-embed changed documents, and a model upgrade requires re-embedding the full corpus since vector spaces aren't compatible across models.