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.
这种 “把鸡蛋放一个篮子里” 的玩法，在政策和竞争的双重冲击下，注定不堪一击。
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为官一任、施政一方，如持卷应答，惟有认真审题、科学破题，“坚持具体问题具体分析，‘入山问樵、入水问渔’，一切以时间、地点、条件为转移”，才能“真正把情况摸清、把问题找准、把对策提实”，做到“一把钥匙开一把锁”。
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