  This paper presents CaseGPT, an innovative approach that combines Large
Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology to
enhance case-based reasoning in the healthcare and legal sectors. The system
addresses the challenges of traditional database queries by enabling fuzzy
searches based on imprecise descriptions, thereby improving data searchability
and usability. CaseGPT not only retrieves relevant case data but also generates
insightful suggestions and recommendations based on patterns discerned from
existing case data. This functionality proves especially valuable for tasks
such as medical diagnostics, legal precedent research, and case strategy
formulation. The paper includes an in-depth discussion of the system's
methodology, its performance in both medical and legal domains, and its
potential for future applications. Our experiments demonstrate that CaseGPT
significantly outperforms traditional keyword-based and simple LLM-based
systems in terms of precision, recall, and efficiency.
