转机出现了，下滚的牛被两棵树挡住，随即弹进了树旁的深坑，卡在坑里喘着粗气。几经周折，这头牛被拉出了坑，但它被重重摔过两次，早没了力气，没走几步路就四仰八叉地下滑，后面卧在沟底不动弹了。
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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.
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