Abstract
This study works on the development of a generative artificial intelligence (AI) university support system (GenAI-USS) by improvising retrieval augmented generation (RAG) architecture to improve the performance of large language models (LLM) in a way that supports stepwise transparency. We aim to achieve better transparency and flexibility, and improved accuracy of responses to queries based on university data assimilated from university webpages and knowledge sources. We use RAG to develop a plug-and-play mechanism, along with prompt selection to boost LLM accuracy. One of the key components in our GenAI-USS is the capture and integration of real-time information via live retrieval into the generative AI process. This domain-specific knowledge assimilation with real-time updates to capture changes and new information serves as a specialized dynamic expert knowledge database for RAG. Our RAG mechanism pulls in relevant, up-to-date information from the dynamic database, which pulls real-time data from targeted predetermined knowledge sources. The other key component in our GenAI-USS design is the deliberately designed information processing visibility at each stage of the process to ensure full transparency, and this includes the following: overview, data collection, storage encoding, testing, chatbot interaction, and search. The testing module allows for interactive viewing of generated responses and their sources. Our strategy is expected to lead to higher-quality AI-generated output via targeted information retrieval, hallucination mitigation, accuracy improvement, and timely data updates. Essentially, on the submission of a query, the RAG-dependent GenAI-USS first identifies the most relevant information from the specialized expert knowledge database and then factors this into the generative AI response development process. This results in a successful implementation of our primary objectives of a transparent and flexible user-choice–driven RAG-based generative AI system, which also provided heuristically notable improvements in the quality of output produced.