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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The new contract with a different provider was only announced on 5 January this year.。关于这个话题，体育直播提供了深入分析
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第三百零五条 依照本章规定适用外国法律，不得损害中华人民共和国的公共利益。。关于这个话题，safew官方下载提供了深入分析
Cancelling in-flight calls.