LEARNER · GLOBAL
What is RAG? A foundational pattern for grounding AI in your own data
RAG (Retrieval-Augmented Generation) means: instead of training an AI model on your data, you keep your data separate in a searchable database. When the AI gets a question, it first *retrieves* relevant documents from that database, then *generates* an answer grounded in those documents. Like giving the model a reference library to cite from, rather than making it memorize everything. Critical for BFSI: lets banks keep data proprietary, reduces hallucinations, ensures compliance by tracing answers back to source documents.
WHY IT MATTERS
Every bank's AI strategy now includes RAG. It's the pattern that lets you deploy generative AI on sensitive data (customer records, contracts, transaction logs) without retraining the model or uploading data to third parties. Understanding RAG is table stakes for technical compliance reviews.
Source: Industry standard · 2026-09-25