# Embeddings

**Source:** https://promtable.com/glossary/embeddings

> Embeddings are dense numeric vectors that represent the meaning of text, images, or other data, allowing similarity to be measured as vector distance.

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Embeddings are dense numeric vectors that represent the meaning of text, images, or other data, allowing similarity to be measured as vector distance.

An embedding model maps a piece of content into a vector (typically 384, 1024, or 3072 dimensions) where semantically similar inputs land close together. This unlocks semantic search (find documents about the same idea, not just the same words), clustering, classification, and RAG. Popular embedding models in 2026 include OpenAI text-embedding-3-large, Voyage AI voyage-3, Cohere embed-v3, and open weights like nomic-embed and BGE. Embeddings are paired with a vector database (Pinecone, Qdrant, Weaviate, pgvector, LanceDB) for efficient nearest-neighbor lookup.

## When to use

- Semantic search over text corpora.
- Clustering related content (FAQs, support tickets).
- Powering the retrieval step in RAG.

## Common mistakes

- Mixing embeddings from different models in the same index — distances are not comparable.
- Skipping normalization (most use cases want L2-normalized vectors for cosine similarity).
- Re-embedding the whole corpus on a prompt change — embed documents once, change the query side.

## Related terms

- [rag](https://promtable.com/glossary/rag)
- [vector-database](https://promtable.com/glossary/vector-database)
- [semantic-search](https://promtable.com/glossary/semantic-search)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/embeddings
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