AI glossary · Building and running AI
Embedding
An embedding is a list of numbers that represents the meaning of a piece of text, an image or another item, so a computer can measure how similar things are.
Also called vector embedding, word embedding, vector
In more detail
An embedding model turns each item into a point in a mathematical space. Items with similar meanings end up close together, even if they use different words: “car” sits near “vehicle” and far from “banana”.
Embeddings power semantic search, recommendations and retrieval-augmented generation. They are usually stored in a vector database.
Example
“How do I reset my password?” and “I forgot my login” get similar embeddings, so a help centre can match them.
Related terms
Semantic searchSemantic search finds results based on what you mean rather than the exact words you type, using AI to understand the intent behind a query.Vector databaseA vector database stores embeddings and quickly finds the ones most similar to a query, which lets AI apps search by meaning across large collections.Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) makes AI answers more accurate by first looking up relevant information, then having the model answer using it.ClusteringClustering is a machine learning technique that groups similar items together without being told the groups in advance.
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