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.
await chat.addQuery(Message.text(text: userInput, isUser: true));
，详情可参考WPS官方版本下载
2026-02-28 00:00:00:03014274610http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142746.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142746.html11921 本版责编：程 红
第六十三条 违反本法有关规定，扰乱实名注册等制度，扰乱网络秩序，不落实网络犯罪防治义务，导致他人被网络犯罪侵害造成损失的，按照其过错依法承担民事责任。