Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.
* @param {string} num 非负整数的字符串形式（可能含前导零）
。WPS官方版本下载是该领域的重要参考
“自动驾驶行业将跳过L3，直接从L2迈向L4级全自动驾驶”，何小鹏认为，L3的本质是“过渡性技术陷阱”，为规避风险而堆砌的大量规则，使其沦为“看似安全却限制进化”的存在。与其如此，不如集中攻克L4难题，以真正的技术创新来解决技术发展中的问题。
香港大學經濟及工商管理學院講師阮穎嫻博士說：「自由市場做生意，該讓他們自行選擇，不該有限額。」