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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.
。关于这个话题，爱思助手下载最新版本提供了深入分析
But those tricks, I believe, are quite clear to everybody that has worked extensively with automatic programming in the latest months. To think in terms of “what a human would need” is often the best bet, plus a few LLMs specific things, like the forgetting issue after context compaction, the continuous ability to verify it is on the right track, and so forth.。同城约会是该领域的重要参考
信任幼儿园，也信任孩子：老师是伙伴，不是「托管员」；孩子比我们想象中更有韧性。，详情可参考91视频
VITURE成立于2021年，总部位于北京和旧金山，创始人姜公略毕业于哈佛大学，是前谷歌眼镜的成员，深度参与了谷歌眼镜的研发。