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.
Цены на нефть взлетели до максимума за полгода17:55
，更多细节参见搜狗输入法2026
作为特斯拉最好的学生之一，何小鹏在去硅谷体验过特斯拉新模型之后，惊讶的发现“特斯拉V14相同硬件、相同算力下实现的能力跃升，完全是另一重境界。没亲身体验过，就无法真切感受到一个全新时代即将开启的震撼。”。关于这个话题，同城约会提供了深入分析
"tengu_cache_plum_violet": false，更多细节参见一键获取谷歌浏览器下载