Why production RAG systems give confident, wrong answers at scale

Production RAG can sound fluent while still answering incorrectly when the right evidence is not retrieved.

At scale, larger corpora, metadata filters, permissions, duplicates, and latency make simple top-k vector search unreliable.

The core risk in enterprise RAG is retrieval quality, not model quality: missing context leads to confident hallucinations.

More robust designs use wider candidate retrieval, early filtering, and multi-stage reranking instead of one-step similarity search.