Vector Database Testing
Vector database testing verifies that a system storing embeddings (for RAG, semantic search, or recommendation) returns the right neighbors for a given query vector — checking index accuracy, latency under load, and that updates and deletes are reflected correctly, since a wrong or stale vector index quietly breaks every downstream AI feature built on it.
A vector database is the retrieval layer underneath most RAG and semantic-search features, and it fails in ways that don't look like a normal database bug — a query doesn't error out, it just returns confidently wrong "nearest neighbors," and nothing downstream knows the difference. Testing has to check both correctness (does a known query vector actually retrieve the document it should, above the similarity threshold the app relies on) and staleness (does a deleted or updated document actually disappear from results, or does the index lag behind the source of truth).
In practice this means building a small set of query-vector-to-expected-document pairs as a regression suite, re-running it after every re-indexing or embedding-model change (since swapping embedding models silently invalidates an existing index — old and new vectors aren't comparable), and load-testing retrieval latency separately from generation latency, since a slow vector lookup adds directly to a RAG feature's end-to-end response time.
The failure mode teams miss most often: index drift after bulk deletes, where the vector count in the index no longer matches the source table and stale results keep surfacing weeks after the "delete" ran.
