## 4 Test Results and Discussion
### Test Results  
To evaluate the impact of different text segmentation methods on our RAG system, we tested fixed-size chunking (FSC), sliding window chunking (SWC), paragraph-based chunking (PBC), sentence-based chunking (SBC), and entropy-optimized dynamic text segmentation (EDTS) on the expert question dataset mentioned in Section 4.1. The scenario where LLMs are directly queried without providing domain-specific data was also tested and included as a comparison baseline. Figure 6: Type distribution of test questions. In this comparative test, we used the Qwen2.5:7B model, selecting the 10 chunks with the closest semantic relevance for each question. The test results, which compare the effectiveness of these methods within our RAG system, are summarized in Figure 7. The EDTS method demonstrates relatively high accuracy across three problem categories, achieving an accuracy rate of approximately 77% in Type I and Type II problems while maintaining competitive performance in Type III problems in which the overall test accuracy has also reached 76%. Standards and specifications documents, characterized by standardized textual structures and precise professional descriptions, represent term-dense exemplars within unstructured knowledge. Their requirement for unambiguous expression typically results in professional terminologies and high determinacy at sentence level. Although longer texts potentially carry more information, comparative testing reveals that FSC-512 configuration shows no accuracy advantage over FSC-128. The choice of LLMs plays a crucial role in determining the outcomes. To understand the differences in performance across various model sizes, we performed the tests using six different models: 1.5B, 3B, 7B, 14B, 32B, and 72B, while keeping the text segmentation method constant. This approach allows us to analyze the effects of model size on performance metrics such as accuracy and runtime. The detailed results of these tests are shown in Figure 8, providing insights into how each model performs under identical conditions. The general trend indicates improved overall accuracy with increasing LLM parameters, though Type III problems exhibit a local maximum at 7B model scale followed by slight accuracy reduction. Figure 7: Comparison of different segmentation methods (LLM = Qwen2.5:7b, top_k = 10). The expansion of LLM scale demonstrates a progressive improvement in question-answering accuracy, while simultaneously incurring a significant increase in execution runtime as shown in Figure 9. This observation necessitates a balanced consideration of application scenarios, target accuracy thresholds, and real-time performance requirements when selecting optimal model scales for practical deployment. Therefore, under current operational conditions, the 7B model represents an optimal balance between accuracy and execution latency. In the practical application of RAG-enhanced specialized LLMs, a critical consideration is the trade-off between model scale and deployment costs. This study's performance comparison reveals that while a 72B parameter model offers higher accuracy, its computational resource requirements may exceed the practical constraints of most engineering sites. Conversely, a 7B parameter model achieves acceptable precision levels for common queries, providing an economically viable solution for the majority of application scenarios. For more complex problems, an on-demand invocation strategy of LLMs can be employed to achieve an optimal balance between performance and cost. This approach not only minimizes unnecessary computational resource expenditure but also ensures efficient operation across tasks of varying complexities in real-world applications. Moreover, the knowledge base developed in this research extends beyond mere knowledge querying; it can be integrated into design or management software to conduct real-time compliance checks of construction plans against the latest industry standards, flagging  
Figure 8: Accuracy comparison under different scales of LLMs.