LEARNER · GLOBAL
What is Retrieval-Augmented Generation (RAG) and why banks use it for LLMs
RAG is a technique that augments a large language model (LLM) with real-time access to external documents or databases before answering a question. Instead of relying solely on its training data, the LLM retrieves relevant information on-the-fly and uses it to generate more accurate, current answers. In banking: compliance officers use RAG-powered chatbots to query policy manuals, risk rules, and regulatory guidance without retraining the model every time rules change.
WHY IT MATTERS
RAG solves the 'hallucination' problem: an LLM without RAG can confidently invent false facts; with RAG, it grounds answers in sourced documents, reducing compliance and fraud risk. It's the de facto architecture for production bank chatbots and Q&A systems.