LEARNER · GLOBAL
What is RAG? How banks use retrieval-augmented generation to ground LLMs in live data
RAG (Retrieval-Augmented Generation) is a technique where an AI language model retrieves relevant documents or data from a database before answering a question—ensuring answers reference current, factual information instead of hallucinatory guesses. In BFSI, banks use RAG to let chatbots cite real customer records, regulatory docs, or market data without retraining the model.
WHY IT MATTERS
RAG is the practical bridge between general LLMs and production BFSI use cases; without it, GenAI chatbots and document-processing agents fail regulatory audit (no source lineage) and generate wrong answers.
Source: Tearsheet · 2026-07-20