Publication Authors: Dijana Oreski

Dijana Oreski

Retrieval-Augmented Generation (RAG) systems address critical limitations of large language models (LLMs) such as hallucinations, static knowledge, and context constraints-by dynamically integrating organizational data to enhance accuracy and relevance in knowledge management (KM). This RAG LLM development integrates the generative strengths of large language models with the precision of real-time information retrieval, enabling responses grounded […]

IN: Proceedings IFKAD 2025: Knowledge Futures: AI, Technology, and the New Business Paradigm
1523-1528
Bozidar Klicek, Ivan Jurinjak, Dijana Oreski

Purpose – In today’s society, knowledge is the most important capital and it is difficult to control. Knowledge is increasingly obtained outside the company. Social networks have taken the lead because they allow the exchange of information in real time. The aim of this study is to determine if the exchange of knowledge in the […]

IN: Proceedings IFKAD 2014 – Knowledge and Management Models for Sustainable Growth
402-425