AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Embeddings are numerical representations of text, images, or other data that let software measure how similar two things are in meaning. Embeddings are the engine behind semantic search and RAG. They are why a search for cancel my plan can find a document titled subscription termination.
Embeddings turn meaning into coordinates on a map. Related concepts cluster nearby; unrelated ones sit far apart. That is how a search for 'cancel my account' can find a document titled 'subscription termination policy.'
A B2B SaaS company reindexes its entire documentation library as embeddings. Support queries now surface the right article even when the user's phrasing matches nothing in the title or tags.
Whenever you need semantic search, document similarity, or the retrieval layer of a RAG system. Embeddings are what make meaning-aware search possible.
For exact-match lookups, such as a database query by ID, or for very short structured data where traditional keyword search is simpler and faster.
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