Retrieval-Augmented Generation in Information Science: What Do We Need to Know?
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Abstract
Information Science is a field that naturally investigates information, that is, contextualized data, its phenomena and interactions, as well as its impacts on society. With the significant increase in data in digital environments and the rapid evolution of Generative Artificial Intelligence (GAI), these sources have taken on new prominence, as they constitute the raw materials for GAI models. However, identifying how this data and information will be collected, processed and organized for use and retrieval within these models is the central question of this study, which investigates the augmented generation of retrieval, or Retrieval-Augmented Generation (RAG). This research aimed to investigate how the RAG technique has been implemented and used today, in light of information organization and representation, in 2026, through a review of the academic and scientific literature. It was observed that RAG is a technique widely used across various fields; however, in Information Science, it remains relatively uncommon, despite the fact that the field’s fundamentals of information organization, representation and retrieval align with the technique, which justifies a fresh perspective on the field to bring it closer to Computing techniques related to Information Retrieval.
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