Unmasking Vector Databases: Why Your Production Search Isn't as Smart as Your Demo

Vector databases are fundamentally approximate nearest neighbor search engines, not the intelligent systems they appear to be in tutorials—a critical distinction that explains production failures developers often struggle to debug. Understanding the HNSW algorithm and its tunable parameters reveals the hidden speed-versus-accuracy trade-offs that tutorials conveniently skip. This knowledge is essential for building reliable semantic search and RAG systems that perform consistently in production.