## 5 Discussion and Implications  
In the existing engineering practice, keyword-based searches and manual document reviews such as specification summary sheets were mainly conducted. These manual tasks require a lot of time and effort from many people, and if a technical problem or human error occurs in the document managing, the result will be difficult to recover. However, the method suggested in this paper focuses on understanding natural language queries and searching for information faster and more efficiently than before. Further, it showed the idea of retaining and utilizing the history of user questions and model answers as in-house knowledge by reusing them as training datasets. It shows strength in automatically maintaining, transmitting, and sharing expert knowledge along with efficient knowledge search. Moreover, this knowledge is automatically maintained as the company's knowledge assets. Since personal memory has limitations, this automated procedure will greatly help internal KM. Moreover, the LLM-based KM presented in the study emphasizes natural language comprehension ability to identify query intentions and generate appropriate responses without the need for accurate keyword matching. This allows users to access information more intuitively and efficiently. The results of this study suggest the potential applicability of the proposed approach. To the best of our knowledge, there are no examples of such an approach being used for \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rating**} & \multicolumn{6}{c}{**LLaMA2-13B**} & \multicolumn{6}{c}{**Davinci-002**} \\ \cline{2-11}  & **5** & **4** & **3** & **2** & **1** & **5** & **4** & **3** & **2** & **1** \\ \hline Search for other related specification & 80\% & 3\% & 5\% & 5\% & 8\% & 14\% & 8\% & 9\% & 18\% & 51\% \\ Explicit specification requirement & 29\% & 6\% & 11\% & 13\% & 41\% & 11\% & 21\% & 19\% & 42\% & 7\% \\ How is it determined? & 42\% & 21\% & 11\% & 11\% & 16\% & 26\% & 26\% & 11\% & 32\% & 5\% \\ Refer to [another specification no.] & 68\% & 3\% & 6\% & 13\% & 10\% & 35\% & 29\% & 16\% & 13\% & 6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fine-tuned LLMs performance by response type. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rating**} & \multicolumn{6}{c}{**LLaMA2-13B**} & \multicolumn{6}{c}{**Davinci-002**} \\ \cline{2-11}  & **5** & **4** & **3** & **2** & **1** & **5** & **4** & **3** & **2** & **1** \\ \hline Type 4 & 54\% & 0\% & 4\% & 10\% & 32\% & 18\% & 14\% & 12\% & 38\% & 18\% \\ Type 3 & 54\% & 12\% & 10\% & 12\% & 12\% & 26\% & 26\% & 18\% & 30\% & 0\% \\ Type 2 & 42\% & 6\% & 12\% & 16\% & 24\% & 14\% &navigating and managing technical specifications. It is shown that the GPT-3 model, once fine-tuned, might be able to deliver answers based on the content present in the selected dataset. The digitization and structuring of technical documents into an easy-to-navigate chatbot could potentially streamline the process for new employees who need to understand these documents. Additionally, the transition of a company's technical know-how into a database might serve as a defensive strategy against the possible loss of institutional knowledge. It acts as a tool to bring people together and enhance communication, and allows the organized storage and transfer of unstructured thoughts and notes, etc. [28]. Nonetheless, it should be noted that the AI model's ability to provide accurate answers is directly tied to the information included in the dataset. Thus, it is essential to continuously update and expand the content to maintain the relevance of the responses. Although the fine-tuned model occasionally introduced extraneous details into the responses, the study found that engineers could easily identify and ignore irrelevant or redundant information. This observation suggests that the presence of additional, non-critical details in the model outputs does not significantly affect their usefulness in practical scenarios.