I evaluated the retrieval stack for production AI systems and found that naïve vector search plus RAG act as lazy band‑aids, leading to frequent hallucinations.
In production AI systems, 80% of hallucination errors come from chunking boundary fractures and lack of hybrid dense‑sparse reranking.
A structured knowledge graph combined with deterministic metadata filtering beats unstructured cosine similarity 9 out of 10 times.
Based on these findings I decided to move away from pure vector similarity, adopt hybrid dense‑sparse reranking, and integrate a structured knowledge graph with deterministic metadata filters to improve retrieval quality.