As large language models continue to advance and become more capable, the relationship between prompt engineering (PE) and sample design engineering (SDE) is likely to evolve in several ways. Firstly, with the increasing complexity and sophistication of LLMs, there may be a greater need for tailored prompt designs to guide the models effectively. PE strategies may need to adapt to the specific capabilities and nuances of advanced LLMs to maximize their performance in various tasks. Secondly, as LLMs become more adept at understanding and generating text, the integration of PE and SDE could become more seamless and optimized. Researchers may develop more sophisticated approaches that combine prompt modifications with refined sample designs to enhance LLMs' overall performance in complex tasks. Additionally, advancements in AI research and natural language processing could lead to the development of automated tools or frameworks that streamline the process of integrating PE and SDE for improved LLM applications.