Your RAG pipeline retrieves real documents — and still hallucinates. Here's the retrieve → generate → verify pattern that catches it before your agent acts, with working Python code you can run right now. Your RAG pipeline retrieves three real documents. The LLM reads them. It generates a response that cites those exact sources. Everything looks clean. And it's still wrong about 8–15% of the time. If you've deployed RAG to production, you already know this. The answer looks grounded in the retrieved chunks, but a closer read reveals the model invented a date, swapped a name, overstated a number, or fused two unrelated facts into a single plausible-sounding sentence. The citations point to real documents. The statement the citations supposedly support was not actually in those documents. This is the hardest class of hallucination to catch. It doesn't look like a hallucination. It looks like a correct answer.…