## 3 Research Methodology
### Data Preprocessing  
The raw information is split and reorganized into a Q&A form for each detail. After collecting the raw technical specifications, a comprehensive preprocessing step began. The raw information was meticulously segmented, analyzed, and reorganized into 33 intuitive question-answer pairs. This format was chosen to simulate potential queries that might arise in real-world scenarios, ensuring that the model can be practically applied to support field experts. The reconstruction of the set was done manually by engineers with extensive practical experience.  
Since the length and composition of each specification was slightly different, the number of Q&A sets that could be extracted varied, as shown in Table 2. In the case of companies where the content of the theme was divided into separate specifications, there were cases where the number of Q&A sets that could be extracted was small. From these initial 33 question-answer pairs, questions from all companies were combined for each type (theme) to ensure consistent data application across each company. For instance, questions extracted from AAA were also asked to BBB and CCC, and questions extracted from BBB  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Check Point** \\ \hline Anchor bolt & \(\bullet\) Does it accurately provide design-related numerical values (e.g., stress, material codes) without confusion or error? \\  & \(\bullet\) Check hallucination issue. \\ \hline Corrosion & \(\bullet\) Does it distinguish and respond to subtle differences in textual content per specification? \\ \hline Welding & \(\bullet\) Does it distinguish and respond to subtle differences in content (text, numbers) per specification? \\ seam & \(\bullet\) Check hallucination issue. \\ \hline Positive material & \(\bullet\) Does it reference related document numbers correctly? \\ identification & \(\bullet\) Check hallucination issue. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification types.  
Figure 2: Sample technical specification summary sheet.  
were also asked to AAA and CCC, thereby balancing the questions. The answers are different depending on the specifications of each company. As a result, the initial 33 Q&A pairs increased to up to 99 original pairs.  
This study applied two-step data augmentation techniques to diversify our initial dataset. At first, the authors paraphrased the original Q&A dataset in order to enrich the dataset with different syntactic structures. When fine-tuning an LLM, paraphrasing is effective strategy to enable a model to learn diverse representations [41]. Thus, this study paraphrased each of the 99 questions and 99 answers 10 times, generating an additional 990 pairs. As a result, a total of 1089 Q&A pairs were prepared including original and paraphrased Q&A pairs. Then, 11 questions and 11 answers from each of the 99 original Q&A pairs were cross-matched to produce unique combinations, yielding a final set of 11,979 pairs (\(11\times 11\times 99\)). This approach further strengthened the contextual associations between the questions and answers.  
After confirming the diversity and comprehensiveness of the dataset, the authors split all the data into training and test datasets using stratified sampling. When splitting the datasets, this study considered that the number of companies and specification types in each dataset should be similar. Here, the number of test dataset pairs in this study was intentionally limited to 200. Considering the given spectrum of data variations, this count was deemed sufficient to provide a representative sample for human assessment, encompassing 2% of the total dataset of 11,979 pairs. The remaining 11,779 pairs, constituting 98% of the dataset, were allocated for fine-tuning.