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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Dependency Injection。体育直播对此有专业解读
从问询函中，医线Insight发现了核心医疗构建的三重商业化“护城河”：，详情可参考safew官方版本下载
Юрий Леонов (ведущий редактор отдела «Бывший СССР»)