Strategic Framework for Implementing Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) for Personalized AI in Informatics Engineering: A Case Study of Malikussaleh University

Abil Khairi (Informatics Engineering)
Wahyu Fuadi
Yesy Afrillia

Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

This study develops a strategic framework for integrating Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to support personalized Artificial Intelligence (AI) applications within the Informatics Engineering Department at Malikussaleh University. By utilizing localized datasets, the framework aims to enhance research productivity and improve educational outcomes while prioritizing data privacy and security. The study examines the opportunities and challenges associated with embedding these technologies into the university’s existing infrastructure, proposing a phased approach to adoption. Emphasis is placed on the modernization of academic practices through AI-driven tools that cater to local educational and research needs. The findings offer insights into implementing advanced AI systems that could serve as a model for similar educational settings focused on sustainable AI adoption.