LEARNER · GLOBAL
What is Retrieval-Augmented Generation (RAG) and why BFSI uses it?
RAG is a technique where an LLM (language model) retrieves relevant documents or data from a database *before* answering a question, instead of relying purely on its training. Think: ChatGPT with access to your bank's policy handbook, real-time prices, or customer account ledger. In practice, an BFSI chatbot uses RAG to ground answers in current interest rates, compliance rules, or transaction history—reducing hallucinations (false claims) and keeping answers tied to ground truth.
WHY IT MATTERS
RAG is the workhorse pattern for BFSI AI: it lets firms deploy LLMs safely on regulated tasks without retraining models, and it keeps outputs auditable because you can trace which documents the model pulled. Most bank AI pilots now use RAG to feed LLMs policy docs, rate cards, or customer data.