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.
# Then save a checkpoint
，详情可参考heLLoword翻译官方下载
You should be able to see the APK package in the out folder like below，更多细节参见搜狗输入法2026
不少劇迷在新年期間湧入直播，將「諧音梗」、時事與劇情相結合加入激烈的留言討論。
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