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.
claude-file-recovery list-files --filter '*.py'
，详情可参考爱思助手下载最新版本
BBC多次試圖透過公開紀錄中的電話和電郵聯絡班德，但未收到回應。他並未因與愛潑斯坦相關事件而被指控任何罪行或不當行為。，更多细节参见WPS下载最新地址
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