An analogy that helps
Imagine plotting every sentence on a map where related ideas land near one another. A sentence about pausing a subscription and a sentence about temporarily suspending an annual plan may be close together even though they use different words. An embedding is the coordinate used for that kind of map.
How embeddings are used
- Create an embedding for each document or item.
- Create an embedding for the user’s query.
- Compare the query with stored vectors.
- Return the closest candidates, then apply metadata filters or a second ranking step.
The important limitation
An embedding captures relationships learned by a model; it does not contain a guaranteed explanation of why two items matched. Store the original text and metadata beside the vector so a person or model can inspect the evidence.
Where teams get into trouble
Changing the embedding model can change the geometry of the entire index. If you change models, dimensions, chunking, or preprocessing, treat it as a versioned migration and compare retrieval quality on a known test set.