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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.
协同畅通了研发路径，也打通了成果转化的“最后一公里”。。关于这个话题，同城约会提供了深入分析
是否树立和践行正确政绩观，这其中衡量的标尺是什么？
。Line官方版本下载是该领域的重要参考
const { initialInput, trace, flowName } = traceLog;
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。，更多细节参见Line官方版本下载