A few months back, one of our internal QA engineers asked the AI assistant a straightforward question about overtime pay calculations for a specific state. The system retrieved the right document, generated a confident answer, and the answer was wrong. Not slightly wrong. It cited a tax withholding table that had been updated two quarters earlier, but our vector store was still serving the old version. Nobody noticed for three days.
That incident changed how I think about retrieval-augmented generation (RAG) systems. I’d been building retrieval-augmented generation pipelines for enterprise applications for a while at that point, and I thought retrieval grounding was enough. It’s not. RAG reduces hallucinations, sure. But “reduces” is doing a lot of heavy lifting in that sentence when you’re processing payroll for millions of people.