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.
Flat graphic design, vintage retro
。关于这个话题，搜狗输入法2026提供了深入分析
如果你希望自己手机的 AI 助手不那么具有「侵入性」，同时又不欠缺基础功能的话，那 S26 系列的 Bixby 还真是一个值得考虑的选项。
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