Tools: Building Production-grade Rag Systems For Document Ai: What It...

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.

Source: HackerNoon