LEARNER · GLOBAL
RAG explained: why banks use retrieval-augmented generation to make LLMs smarter
Retrieval-Augmented Generation (RAG) is a technique that lets AI models pull real-time data from external sources (customer records, regulatory docs, market feeds) before answering questions. Instead of relying only on training data, RAG lets the model cite fresh, verified information.
WHY IT MATTERS
RAG is how BFSI avoids hallucinations in customer-facing use cases. Banks use RAG to ground LLM answers in actual account balances, transaction history, and compliance rules—making AI safe for regulated domains.