LEARNER · GLOBAL
What is RAG? How banks use retrieval-augmented generation to ground AI answers in real data
RAG (Retrieval-Augmented Generation) is a technique that lets an AI model search a bank's internal documents—loan policies, account records, compliance rules—and use those results to answer questions accurately. Instead of relying only on what it learned during training, the AI fetches fresh, specific data before responding. Example: a compliance LLM retrieves the latest KYC rules from your policy database and cites them in its answer.
WHY IT MATTERS
RAG solves the 'hallucination problem'—when AI invents plausible-sounding but false answers. For BFSI, this is critical: a mortgage advisor chatbot using RAG won't invent loan terms. Most production bank LLMs rely on RAG to stay current and compliant.