Building Production-Grade RAG Systems for Document AI: What It Actually Takes

Moving RAG from demo to production requires shifting focus from clever prompting to repeatable engineering. Success depends on high-fidelity ingestion (preserving layout and tables), hybrid retrieval (combining vector and BM25), and mandatory security filters. Real-world systems prioritize traceability and "groundedness"—the ability to prove exactly where an answer originated. Monitoring and evaluation against a golden dataset ensure the system stays reliable as documents and models evolve.