LEARNER · GLOBAL
What is RAG? Why banks use retrieval-augmented generation to ground AI on proprietary data
RAG (Retrieval-Augmented Generation) is a technique where an AI model retrieves relevant documents or data from a private database *before* generating an answer. Example: a bank's credit-risk LLM queries historical loan documents and regulatory rulings to answer 'why was this applicant denied?' Instead of guessing, it grounds output in actual bank knowledge.
WHY IT MATTERS
RAG separates LLM knowledge (outdated, generic) from enterprise facts (proprietary, fresh). Without RAG, bank AI hallucinates. With RAG, it can cite sources and comply with audit trails. Essential for compliance and trust.