## 1 Introduction  
Technological advancements and multinational corporations dominate the global economy in the information age and knowledge society. The complexity and scale of corporations and their technical requirements are driving the need for more effective knowledge management (KM) solutions. Particularly, in complex engineering projects such as oil and gas and nuclear power plants, engineering data and documentation are tremendous and should be reviewed in a short bid period. For example, within two months, the contractors in Qatar construction project should have reviewed the engineering specification, which included more than 4000 pages of requirements and 13,000 references [1]. A comprehensive understanding of such a vast body of knowledge requires significant time, and the interconnected nature of multi-disciplinary teams does not lend itself to intuitive or easy understanding. If a contractor fails to comply with the provisions of the technical specifications, the project is exposed to latent risks such as rework, delays, defects, and accidents [2]. According to a McKinsey study, employees spend nearly 20% of their working week searching for internal information or asking colleagues for help [3]. Knowledge transfer from senior engineers to junior engineers is vital for enhancing an organization's competitiveness. However, the construction industry has suffered from the aging of the workforce including managerial employees [4, 5]. The departure of senior professional can lead to significant knowledge gaps, thereby increasing operational risks and posing potential setbacks [6]. This situation is further aggravated by the transitory nature of the project-based industry where temporary project teams are organized, work together for a while, and dissolve after finishing the project [7]. Despite various efforts, due to internal personnel and external environment changes, essential organizational knowledge can fade or even vanish. To address KM challenges, diverse methodologies, and technologies have been deployed over the years including the strategic capturing, storing, using, and sharing of information throughout the company [8]. Traditional databases, manual documentation, and even computer-based solutions have been beneficial; however, they also have limitations such as time-consuming data retrieval processes [9; 10]. With the advancement of natural language processing (NLP) technology, some researchers have attempted to automate the specification document review using NLP [11; 12; 13; 14]. Nevertheless, existing NLP-driven specification document review techniques were limited in terms of the inability to provide contextual insights. In particular, traditional approaches typically require the manual creation of a rule set for the model development and have difficulty understanding the context of input text. As technological advancements continue, newer and more innovative solutions have emerged. Particularly, recent generative artificial intelligence (AI) models offer a revolutionary approach to KM challenges. Global information technology (IT) and AI companies are releasing large language models (LLM) at a rapid pace and up-to-date representative models are ChatGPT and LLaMA introduced by OpenAI and Meta AI, respectively. Fine-tuning these AI models with domain-specific datasets enables a corporation to develop own conversational interface services [15]. It makes a large amount of technical knowledge readily accessible in a user-friendly question and answer (Q&A) format. These AI models represent significant advances over traditional database search tools. Fine-tuned AI models can understand natural language queries, making them more intuitive and user-friendly. They can interpret the intent behind the query, provide contextually relevant answers, and handle ambiguous or poorly structured questions. This not only improves the efficiency of information retrieval, but also improves the quality of the results, leading to a more informed decision-making process [16].