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.
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模型显示，在第一阶段，企业引入 AI Agent 大量裁撤知识型与中介型白领员工，大幅削减人力成本，短期内实现利润率激增与财报繁荣。然而，被裁撤的中产阶级群体恰恰是支撑现代消费经济体系的核心基石。。关于这个话题，服务器推荐提供了深入分析
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