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Most developers view Retrieval-Augmented Generation (RAG) as a tool for answering questions: "What is our refund policy?" or "Show me the transaction history." In high-frequency domains like QR Code Payments, RAG can be integrated into a Predictive Intelligence Workflow. Here, retrieval doesn't just fetch facts; it fetches contextual patterns, historical anomalies, and dynamic risk rules that feed into a predictive model. The LLM then acts not as a chatbot, but as a reasoning engine that synthesizes retrieved data to predict outcomes (e.g., fraud probability, merchant churn, settlement delay) before they happen.
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