LEARNER · GLOBAL
RAG: Retrieval-Augmented Generation explained for compliance teams
RAG is a technique where an LLM first searches a knowledge base (company docs, policies, ledgers) to fetch relevant facts, then answers your question using those facts. Instead of the model making things up from training memory, it pulls live data and cites sources. In banking, RAG powers compliant chatbots that quote actual policy or audit trails.
WHY IT MATTERS
RAG is how fintech vendors make LLMs safe for regulated use: auditable retrieval + fresh data = explainability and reduced hallucination risk. Most production BFSI AI today uses RAG, not raw LLMs.