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This paper presents a comprehensive exploration of the Retrieval-Augmented Generation Architecture Framework (RAGAF), structured around seven key modules: Generator, Retriever, Orchestration, UI, Source, Evaluation, and Reranker (GROUSER). Each module plays a vital role in enabling dynamic information retrieval and contextually relevant response generation. The study discusses the enabling technologies that support these modules, including large language models, vector databases, knowledge bases, text processing, frontend technologies, orchestration tools, and hardware accelerators. Through a detailed case study in the domain of customer support, we demonstrate how the RAG architecture framework can enhance the efficiency and accuracy of customer interactions by generating accurate, context-aware responses while reducing human workload. The analysis highlights the strengths and challenges of RAG systems, offering insights into their optimization and deployment to achieve improved customer satisfaction and service quality in practical applications.

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Retrieval-Augmented Generation Architecture Framework: Harnessing the Power of RAG

  • 会议论文

  • 发表日期：

    2025年

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  • Richard Shan,
  • Tony Shan

摘要

This paper presents a comprehensive exploration of the Retrieval-Augmented Generation Architecture Framework (RAGAF), structured around seven key modules: Generator, Retriever, Orchestration, UI, Source, Evaluation, and Reranker (GROUSER). Each module plays a vital role in enabling dynamic information retrieval and contextually relevant response generation. The study discusses the enabling technologies that support these modules, including large language models, vector databases, knowledge bases, text processing, frontend technologies, orchestration tools, and hardware accelerators. Through a detailed case study in the domain of customer support, we demonstrate how the RAG architecture framework can enhance the efficiency and accuracy of customer interactions by generating accurate, context-aware responses while reducing human workload. The analysis highlights the strengths and challenges of RAG systems, offering insights into their optimization and deployment to achieve improved customer satisfaction and service quality in practical applications.