Embedding
A numerical representation of text, images, or other data — used to measure semantic similarity.
What is Embedding?
An embedding is a list of numbers (typically 384, 768, 1536, or 3072 dimensions) that represents the "meaning" of some content. Two pieces of text with similar meaning produce similar embeddings — they are close together in the vector space.
Embeddings are how machines understand semantic similarity. The phrases "I need help with my order" and "Where is my package?" have different words but very similar embeddings — making them findable by semantic search.
You generate embeddings using an embedding model — OpenAI's text-embedding-3-large, Cohere's embed-v3, or open-source models like BGE. The choice matters: dimensions, language coverage, and cost vary significantly.
Embeddings are the foundation of RAG, semantic search, recommendation systems, and clustering. Every Gen AI engineer in 2026 works with them daily.
An Indian e-commerce search engine uses embeddings to match "lehnga for sangeet" to products labelled "Indo-western party wear" — even though the words do not overlap. Conversion rates rose 23% versus keyword search.
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