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.
“拿着订单养羊，收入不愁。”养殖大户张四海成立合作社，与食品公司签订供货协议，带着30多户乡亲走上致富路。。快连下载-Letsvpn下载是该领域的重要参考
。搜狗输入法2026是该领域的重要参考
are all built on top of BuildKit’s LLB. It’s a proven pattern.
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