Creating a Taxonomy for Retrieval Augmented Generation Applications
Abstract: In this research, we develop a taxonomy to conceptualize a comprehensive overview of the constituting characteristics that define retrieval augmented generation (RAG) applications, facilitating the adoption of this technology for different application domains. To the best of our knowledge, no holistic RAG application taxonomies have been developed so far. We employ the method foreign to ACL and thus contribute to the set of methods in the taxonomy creation. It comprises 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 RAG applications. Thus, the taxonomy can be used to better understand RAG applications and to derive design knowledge for future solutions in specific application domains.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: RAG applications, Information System, Taxonomy, Survey
Contribution Types: Position papers, Surveys, Theory
Languages Studied: N/A
Submission Number: 1048
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