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.
企业在 Data+AI 领域面对的挑战
，详情可参考51吃瓜
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And while it used to be a pain to transition from Windows to Mac, it’s far easier these days, especially if you mainly rely on web apps. It also wouldn't be tough for Apple to make short tutorials to help Windows users get their bearings with the macOS basics, like installing apps and juggling app windows. Apple could also make a play for iPhone owners using Windows, who may not be aware of the many ways iOS and macOS are integrated. iPhone mirroring may be a huge draw on its own.