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RAG (Retrieval-Augmented Generation): why banks use it to ground LLMs in real data

RAG is a technique that feeds a large language model (LLM) with live, proprietary data—like customer transactions or policy docs—before answering questions. It reduces hallucinations and keeps answers current without retraining. Banks use RAG in compliance chatbots and customer service to ensure accuracy.

WHY IT MATTERS

RAG is foundational to safe BFSI LLM deployment. Engineers must design vector databases and retrieval pipelines to handle regulatory audit trails.

Source: AITechHive · 2026-09-01

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RAG (Retrieval-Augmented Generation): why banks use it to ground LLMs in real data — ath — AITechHive