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Embeddings

Numerical vector representations of data such as text or images that capture meaning so machines can compare similarity.

By Sitebard TeamUpdated April 15, 2026

In plain English

Embeddings turn words, sentences, or images into lists of numbers that capture their meaning. Things with similar meaning get similar numbers, so a computer can tell what is related.

Technical definition

An embedding is a dense vector in a continuous high-dimensional space produced by a model so that semantically similar inputs map to nearby points under a distance metric such as cosine similarity. Embeddings are foundational to semantic search, clustering, classification, and retrieval-augmented systems.

Business use case

Businesses use embeddings to build smarter search and recommendation features that understand intent rather than matching exact keywords. For example, a support portal can return the most relevant help articles even when a customer phrases a question differently from the documentation.

Example

The phrases 'how do I reset my password' and 'I forgot my login credentials' produce embeddings that sit close together, so a search system returns the same help article for both.

Frequently asked questions

An embedding is a list of numbers (a vector) that represents the meaning of a piece of data, such as a word or sentence, so that similar items end up close together in that numerical space.

Embeddings let computers measure how similar two pieces of content are, which powers semantic search, recommendations, clustering, and retrieval systems that find relevant information by meaning rather than exact keywords.

Embeddings are commonly stored in a vector database, which is optimized to index and quickly retrieve the vectors most similar to a given query.

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