Generative Artificial Intelligence: conceptual and practical analysis of the retrieval-augmented generation model
Keywords:
retrieval-augmented generation, large language models, text generation, generative artificial intelligence, systematic review.Abstract
Objective: To systematically review the status of applications of the retrieval-augmented generation model (RAG) for text generation, the main objective is to make a diagnostic survey, with the secondary purposes of identifying the forms of dissemination preferred by the authors, revealing the topics addressed, the funding entities, and establishing co-authorship networks. Design/Methodology/Approach: Due to its methodology, it is an exploratory-descriptive and bibliographical research. Results/Discussion: The results show that authors prefer to publish their work in conferences, the institution that funds the most research is the National Natural Science Foundation of China, while in the United States the largest number of publications are produced, authors affiliated with universities predominate, and the most attended research topics are large language model systems, question answering, text generation, and the development of knowledge graphs. Conclusions: In the scientific field of information, the RAG model is related to the information retrieval and natural language processing. It overcomes the limitations of large language models by adding specific and updated information from external sources. It has great potential for application in digital humanities research, by taking advantage of the knowledge learned in the parameters of pre-trained large language models with information from specific repositories that could be databases of historical documents, or libraries that can be used to create new services. Originality/Value: It offers a first overview of the RAG model, allowing information professionals to understand this important generative artificial intelligence technology, in addition to knowing its main research funders, the authorship networks between organizations and its main applications in text generation.
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Copyright (c) 2026 Angel Freddy Godoy Viera, José Antonio Moreiro-González
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