  Current research on RAGs is distributed across various disciplines, and since
the technology is evolving very quickly, its unit of analysis is mostly on
technological innovations, rather than applications in business contexts. Thus,
in this research, we aim to create a taxonomy to conceptualize a comprehensive
overview of the constituting characteristics that define RAG applications,
facilitating the adoption of this technology in the IS community. To the best
of our knowledge, no RAG application taxonomies have been developed so far. We
describe our methodology for developing the taxonomy, which includes the
criteria for selecting papers, an explanation of our rationale for employing a
Large Language Model (LLM)-supported approach to extract and identify initial
characteristics, and a concise overview of our systematic process for
conceptualizing the taxonomy. Our systematic taxonomy development process
includes four iterative phases designed to refine and enhance our understanding
and presentation of RAG's core dimensions. We have developed a total of five
meta-dimensions and sixteen dimensions to comprehensively capture the concept
of Retrieval-Augmented Generation (RAG) applications. When discussing our
findings, we also detail the specific research areas and pose key research
questions to guide future information system researchers as they explore the
emerging topics of RAG systems.
