Bridging the Gap Between Large Models and Real‑World Applications with RAG and Agents
This article examines how Retrieval‑Augmented Generation (RAG) and multi‑agent technologies narrow the gap between large language models and practical deployment, highlighting their roles in operations automation, financial risk control, intelligent data governance, database localization, edge inference, and future AI‑driven solutions.