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.
首先社交方面，她交到了很多朋友，每天放学都会说今天跟谁玩了，问她好朋友是谁，能说出很多。跟谁玩什么也都表达的很清楚。而且，还会聊家常了，比如哪个好朋友请假了，去干嘛都会聊。而且也可以跟老师表达自己的需求，比如吃饭不够了会跟老师要，渴了也会跟老师说要喝水等等。
。搜狗输入法2026对此有专业解读
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The Center for Digital Progress (D64) d-64.org🇩🇪
甚至还有一些讲究「降本增效」的防窥膜，只蚀刻纵向光栅，导致只能左右防窥、上下或者斜视看过去依然是一览无余的：