LEARNER · GLOBAL
What is Retrieval-Augmented Generation (RAG) and why it matters for enterprise AI
Retrieval-Augmented Generation (RAG) is a technique where an AI model fetches relevant documents or data from a private knowledge base *before* generating an answer. Instead of relying only on what it learned during training, the model can reference your bank's policies, customer records, or regulatory docs in real time. This makes LLMs safer and more accurate for BFSI use cases like compliance Q&A, customer service, and underwriting.
WHY IT MATTERS
RAG reduces hallucinations and makes LLMs controllable in regulated settings. Most BFSI LLM pilots (compliance automation, underwriting, risk docs) depend on RAG to stay compliant; understanding its limits—latency, retrieval quality, data freshness—is critical for production roadmaps.