Leveraging Large Language Models for Document Analysis and Decision-Making in AI Chatbots | ||||
| Advanced Sciences and Technology Journal | ||||
| Volume 2, Issue 1, June 2025, Page 1-16 PDF (527.37 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/astj.2025.342484.1034 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Islam M. Ibrahim ; Mahmoud Soliman Attallah; Shahd Osama Abdel Hamid; Sherif Tarek Zween; Iyad Abuhadrous 
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| Software Engineering and Information Technology, Faculty of Engineering and Technology, Egyptian Chinese University, Cairo, Egypt | ||||
| Abstract | ||||
| The rapid improvement of Large Language Models (LLMs) is transforming how businesses handle document workflows, making the process faster and smarter. This paper explores how businesses can use LLMs to create AI chatbots that enhance document analysis and improve decision-making. These chatbots can perform tasks like extracting key information, summarizing content, and generating insights, saving both time and resources for businesses that work with a large volume of written materials. They also make quick, informed decisions easier by providing accurate, context-based responses. However, despite their benefits, these systems face challenges like ensuring data security, managing high computational demands, and addressing ethical concerns such as bias and transparency. The paper also examines modern advancements and future possibilities, highlighting how this technology could reshape the way businesses handle documents and make decisions. It emphasizes the potential of LLMs to deliver smarter, safer, and more efficient tools that adapt to the ever-changing needs of modern organizations. | ||||
| Keywords | ||||
| LLMs; Chatbots; Decision-Making; RAG; Document Analysis | ||||
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