LEARNER · GLOBAL
What is RAG? Why banks use it to ground AI on proprietary data
Retrieval-Augmented Generation (RAG) is a technique where an AI model first retrieves relevant documents or data chunks from a database, then generates answers based on that context. Instead of relying only on facts baked into the model during training, RAG pulls in fresh, proprietary, or real-time information—like a bank's loan policies, customer history, or regulatory filings—and uses it to make the answer more accurate and current. Think of it as giving the AI a reference library to consult before answering.
WHY IT MATTERS
RAG lets banks deploy LLMs on sensitive internal data without retraining the entire model. A compliance officer can ask about policy interpretation over the bank's actual policy documents; the AI retrieves the exact clause and generates a grounded answer. Reduces hallucination risk and keeps proprietary data private.