Embedding Quality Testing
Embedding quality testing checks whether the vector representations a RAG or search system uses to compare text actually place semantically similar content close together and dissimilar content far apart — since a retriever is only as good as the embeddings it ranks candidates by, regardless of how well the rest of the pipeline is built.
A retrieval failure is often blamed on the wrong part of the system — the chunking, the prompt, the model — when the actual root cause is an embedding model that doesn't represent the domain well. A general-purpose embedding model trained mostly on web text can perform noticeably worse on specialized content (legal contracts, medical notes, internal jargon-heavy documentation) where the surface wording differs from prose it was trained on, even if the meaning is the same.
Testing it means building a set of known similar and known dissimilar text pairs specific to the actual domain, embedding both, and checking that similarity scores (typically cosine similarity) correctly separate the two groups — pairs that should be considered related consistently scoring higher than pairs that shouldn't.
It's also worth testing embedding stability across model or provider updates — a vendor's silent embedding model update can shift the vector space enough that a previously well-tuned retriever's relative rankings degrade, with no code change on the application side to point to.
