Figure indicates that the results for the multi-hop task were similar to those for the single-hop task. Claude and Gemini continued to perform exceptionally well. Notably, GPT-4 showed slight improvements in both groundedness and precision, with significant increases in accuracy and hallucination capture rate, bringing them closer to the levels achieved by Claude and Gemini. As previously reported , this improvement may be attributed to GPT-4 "thinking" more thoroughly when responding to the revised prompt, leading to better self-correction when faced with hallucinated questions in the multi-hop task compared to the single-hop task. This highlights the importance of prompt engineering in leveraging the full capabilities of LLMs, especially when dealing with nuanced, multi-step reasoning tasks. Moreover, recent work has shown that prompt engineering can help encourage the generation of Q&A pairs with multi-hop reasoning, which helps better differentiate the capabilities of various LLMs and push the boundaries of their understanding. During our evaluation, we found that although the prompt did not explicitly require it, Gemini proactively numbered the different types of questions (Figure ), resulting in more organized responses. This approach suggests a higher level of understanding of structured data generation. To quantify this behavior, we introduced an alignment parameter (Table ) to compare how well different LLMs adhered to formatting requirements. As shown in Table 1, GPT-4's responses rarely followed the expected format, approximately half of Claude's responses were organized, and nearly all of Gemini's responses met the formatting criteria. This difference in adherence reflects the models' varying abilities to interpret implicit organizational cues. We believe that adherence to proper formatting is crucial for creating structured datasets in the future, as it significantly enhances ease of use, reduces the need for manual adjustments, and facilitates subsequent analysis. Overall, based on these findings, we conclude that Gemini offers several advantages in generating Q&A datasets: high accuracy, well-structured responses, and lower cost compared to other models. Moreover, Gemini's ability to independently impose structure on its outputs suggests that it is particularly well-suited for applications where consistency and ease of integration are paramount.