## 1 Introduction & Background
This integration ensures accurate and contextually relevant answers, significantly improving the performance of existing systems and offering a robust solution for handling complex, multi-round dialogue questions in digital government applications [9]. A transformative approach to enhancing the accessibility and usability of Open Government Data (OGD) through LLMs is demonstrated by employing the GPT-3.5 model to interact with the Scottish open statistics portal. This study shows how LLMs can transform natural language queries into precise SPARQL queries, enabling non-technical users to retrieve accurate statistical information effortlessly. The innovative application of Retrieval Augmented Generation (RAG) ensures factual correctness in responses and proposes a scalable, user-friendly solution for democratizing access to government data [10]. The handling of the massive entry of citizen complaints in e-government systems using advanced data mining algorithms showcases another practical application of LLMs. By comparing various algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost on a large dataset from the LAKSA app in Tangerang City, Indonesia, the study identifies the most accurate classifier and highlights the importance of continuous supervised training. This research demonstrates the potential of data-driven methods to improve the efficiency and accuracy of complaint management, enhancing citizen satisfaction and offering a scalable solution for smart governance [11]. In the business sector, the development of the Persuasive Message Intelligence (PMI) service introduces advanced Generative AI (GAI) technologies for automated personalized marketing. Utilizing prompt engineering techniques based on persuasive marketing theories and validated through surveys, PMI generates tailored marketing messages that significantly enhance customer engagement. This study addresses the high costs and complexity of manual message creation by leveraging OpenAI's GPT-4 model, ensuring cost-effective and scalable solutions [12]. Another study presents AssaraBot, an AI chatbot designed to enhance Content and Language Integrated Learning (CLIL) by teaching cultural content about the Minoan Civilization and facilitating language learning in English and French. This innovative approach leverages AI for interactive and personalized education, supporting human tutors and addressing remote learning challenges. Evaluated in Greek schools, AssaraBot demonstrates significant advancements in ICT-based education, contributing to improved methodologies in educational technology and language acquisition [13]. Additionally, a novel approach for federal agencies to systematically report on their AI use cases has been introduced, emphasizing transparency, accountability, and effective governance. This draft guidance provides a comprehensive set of criteria and mechanisms for annual AI inventories, focusing on the management of AI-related risks and ethical implications. The framework's innovative approach to safety-impacting and rights-impacting AI use cases sets a new standard for AI governance within the federal sector [14]. The transformation of legal texts into machine-executable computational logic represents another significant advancement. By utilizing Natural Language Processing (NLP) techniques and a structured document inference process, legal provisions are converted into Prolog predicates, making legal rules transparent and explainable. This method addresses the challenges of ambiguity and context dependency in legal texts, providing a foundation for automated legal reasoning and decision-making [15; 16]. Finally, the use of LLMs in transforming legal texts into digitized public services via No-Code/Low-Code platforms showcases the potential for reducing manual effort in process digitization. By integrating advanced NLP techniques and formal process modeling standards, this research enhances transparency, efficiency, and accessibility in public service delivery [17; 18].