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  • A Systematic Literature Review of Retrieval-Augmented . . .
    This systematic review of the research literature on retrieval-augmented generation (RAG) provides a focused analysis of the most highly cited studies published between 2020 and May 2025 A total of 128 articles met our inclusion criteria The records were retrieved from ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and the Digital Bibliography and Library Project (DBLP) RAG
  • Retrieval-Augmented Generation to Generate Knowledge Assets . . .
    This article explores the application of Retrieval-Augmented Generation (RAG) to enhance the creation of knowledge assets and develop actionable insights from complex datasets It begins by contextualising the limitations of large language models (LLMs), notably their knowledge cut-offs and hallucination tendencies, and it will present RAG as a promising solution that integrates external
  • Retrieval-Augmented Generation (RAG) - Springer
    Retrieval-augmented generation (RAG) has been proposed as a new framework for AI that seeks to integrate additional knowledge, such as organizational data, and generate results that can be linked to that knowledge (Lewis et al 2020) This allows users to access information from within an organization and reduces the risk of hallucinations
  • Retrieval-Augmented Generation to Generate Knowledge Assets . . .
    The results of this study highlight both the promise and the complexity of using Retrieval-Augmented Generation (RAG) systems for knowledge asset generation and action driver creation
  • Advancing Retrieval-Augmented Generation (RAG) Innovations . . .
    PDF | Retrieval-Augmented Generation (RAG) has emerged as a transformative approach in artificial intelligence (AI), enhancing large language models | Find, read and cite all the research you
  • Enhancing Retrieval-Augmented Generation: A Study of Best . . .
    Abstract Retrieval-Augmented Generation (RAG) sys-tems have recently shown remarkable advance-ments by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually rel-evant responses However, the influence of various components and configurations within RAG systems remains underexplored A com-prehensive understanding of these
  • Retrieval-Augmented Generation (RAG): Trends, Architectures . . .
    Abstract Retrieval-Augmented Generation (RAG) is an emerging approach that enhances language models by integrating document retrieval into the generation process This paper provides a comprehensive study of RAG systems, examining their architecture—comprising retrievers, fusion techniques, and generators—and their performance across knowledge-intensive tasks We explore the historical





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