LEARNER · GLOBAL
What is retrieval-augmented generation (RAG), and why does it matter for banking AI?
Retrieval-augmented generation (RAG) is a technique where an AI model pulls real-time information from external databases—like customer transaction histories or regulatory documents—before generating an answer. Think: ChatGPT that consults your bank's internal ledger before answering a customer question about their account. This keeps responses accurate and current, avoiding AI hallucinations.
WHY IT MATTERS
RAG is the gold standard for production BFSI AI because it grounds responses in real data (customer accounts, balances, transaction rules) rather than relying solely on training data that may be stale. Without RAG, an LLM chatbot might invent account balances; with it, it retrieves the true balance from your core banking system.