Stale RAG Indexes, Not AI Models, Are Often the True Source of Hallucinations

Retrieval-augmented generation (RAG) systems can produce confidently wrong answers not because the AI model fails, but because the vector index it queries contains outdated information. Developers often misattribute these errors as model hallucinations, when the real cause is a freshness failure in the data pipeline. A RAG index can go stale in three distinct ways: existing documents whose facts have changed, new content that was never crawled, and removed content that still lingers in the index. Engineers are advised to treat the retrieval index as a continuously managed cache rather than a one-time build, applying change detection and targeted re-embedding instead of full re-crawls. Addressing index freshness is framed as a pipeline engineering problem with actionable solutions, rather than a model-tuning challenge.