Retrieval-Augmented Generation (RAG) is an AI technique that enhances large language models by integrating external, up-to-date information to improve the reliability and relevance of AI outputs. Traditional RAG architectures involve data processing, retrieval mechanisms, and generation phases to produce accurate responses.
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How Agentic RAG Works: Understanding Agentic RAG’s ArchitectureWhat is Retrieval-Augmented Generation (RAG)Understanding Traditional Retrieval-Augmented Generation (RAG) ArchitectureWhat are Agents in AI ?Understanding Agentic Retrieval-Augmented Generation (RAG) ArchitectureBenefits of Agentic RAGApplications of Agentic RAGLimitations of Agentic RAGConclusionSort: