Glossary · 4 · Grounding: content, retrieval and knowledge

Embedding

Also known as: Vector embedding, Semantic search

An embedding is a list of numbers — a vector — that represents the meaning of a piece of text, an image or other data, produced by an embedding model so that items with similar meaning end up close to each other in vector space.

Intermediate
Technical writers
Developers

In one sentence

Embeddings explained: turning text into vectors that capture meaning — the basis of semantic search and retrieval-augmented generation.

Example

“Replace the filter cartridge” and “How do I change the filter?” use different words, but their embeddings are close, so a semantic search finds the right topic.

Why it matters on your learning path

Technical writers: Embeddings are why AI search finds content by meaning, not keywords — but consistent terminology still improves results.
Developers: Choose an embedding model for your languages and domain; re-embed content when you switch models.