Semantic search was a breakthrough. It is also incomplete. The production systems that work in 2026 use something different.
The Query That Breaks Every Pure Semantic System
A user types: "SKU-48291 return policy."
A pure vector search converts that into an embedding and looks for conceptually similar content. It finds documents about return policies. It finds documents about product codes generally. It does not find the specific document for SKU-48291, because the exact identifier is not a semantic concept. It is a string.
This is the failure mode that pure vector search has always had and that most teams only discover in production. Semantic search is extraordinarily good at understanding meaning and intent. It is not good at exact matching. Product codes, error codes, person names, regulatory references, contract clause numbers, legal citations: any query that depends on matching a specific string exactly is a query that vector search handles poorly.
