Project documentation plays a critical role in effective project management, yet extracting relevant information from large and complex PDF documents remains a challenging and timeconsuming task. Project managers and team members often spend significant effort manually searching through documents such as requirement specifications, project plans, risk registers, and status reports to locate essential information. Traditional project management tools mainly focus on task tracking and collaboration but lack advanced capabilities for intelligent document understanding and contextual retrieval. To address these limitations, this research proposes an AIpowered Project Management Assistant Chatbot that leverages Retrieval-Augmented Generation (RAG) architecture to enable efficient information extraction and conversational interaction with project documents. The system processes uploaded PDF files by converting them into machinereadable text, performing semantic chunking, and generating vector embeddings using advanced natural language processing models. These embeddings are stored in a vector database that enables fast and contextually relevant document retrieval. When users ask questions, the system retrieves the most relevant document sections and provides accurate responses through a large language model. The chatbot interface allows project managers and team members to interact with project documentation through natural language queries, significantly reducing the time required to locate information. The proposed system enhances decision-making, improves knowledge accessibility, and supports efficient project monitoring. Experimental observations demonstrate that the system can effectively retrieve context-relevant information and provide meaningful answers from project documents. This approach contributes to intelligent document management in project environments by combining natural language processing, semantic search, and conversational AI technologies.
JSETMS (2026) studied this question.