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R.A.G. (Retrieval Augmented Generation) is a powerful technique for enhancing Large Language Model (LLM) outputs with real-time, external data. RAG bridges the gap between static model knowledge and dynamic, context-aware responses.
Join hosts Brian Fehrman, Derek Banks, Bronwen Aker, and Ben Bowman as they break down how RAG improves the reliability and relevance of generative AI systems. You’ll learn why context retrieval matters, what problems RAG solves, and where it fits into modern AI security practices.
