LEARNER · GLOBAL
What is RAG? How banks ground AI in real data
RAG (Retrieval-Augmented Generation) combines a large language model with a search engine over your own documents or databases. Instead of relying only on what the AI was trained on, RAG pulls relevant facts from your bank's records—client data, compliance docs, transaction history—and uses those to answer questions accurately. Example: A bank's LLM uses RAG to fetch a customer's actual account history before drafting a credit decision letter, ensuring the AI doesn't hallucinate.
WHY IT MATTERS
RAG is the most production-ready pattern for bank AI because it reduces hallucinations and keeps responses grounded in verifiable data; critical for compliance, credit, and customer-facing use cases.