One promising solution to address these limitations is to enhance LLMs with domain-specific knowledge. This involves cross-disciplinary efforts, merging natural language processing (NLP) with expert insights.

In this master thesis proposal, my primary goal in the literature review is to investigate the inherent deficits and limitations and respectively their current solutions, as well as applications of LLMs in information retrieval.

I have deployed design science research combined with case study as my research strategy. This research demonstrated this approach by developing a knowledge base for the "Master of Science in Business Information Systems" programme at the FHNW University of Applied Sciences and Arts Northwestern Switzerland (FHNW). This knowledge base was integrated with a language model, specifically tailored to the organisation by using retrieval augmented generation (RAG)-based technology with programme-related documents. The aim was to create a practical, testable solution that efficiently assists students.