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.
// 注意：slice(0, 负数)会返回空字符串，需兼容（比如stack长度<k时，slice后为空）
。业内人士推荐Line官方版本下载作为进阶阅读
园所性质：这个我觉得跟费用是挂钩的，看个人情况，私立我就不提了没那个财力不考虑，简单说一下公办、普惠的区别：
Onboarding gets simpler. Instead of “here’s the .env file, don’t lose it,” you say “set up 1Password and run the setup script.” The secrets are in the vault with proper access controls.
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