What it means
An embedding model turns a piece of text into a long list of numbers. Texts with similar meaning get similar numbers, even if they use different words. "How do I reset my password" and "I forgot my login" end up close together.
Embeddings are stored in a vector database and searched by similarity. They are the search layer behind most knowledge assistants.
Why it matters for a business
Search by meaning finds answers that keyword search misses, which makes help centers, document search and assistants far more useful. Changing embedding models later means re-processing every document, so the choice deserves a test.
A business example
Things to watch
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Switching models means re-embedding every document.
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Test retrieval with real questions.
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Combine with keyword search for exact terms such as codes.
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Keep the source text and metadata with every vector, so answers can cite where they came from.
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Re-embed documents when they change, or search results drift out of date.