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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.
。下载安装 谷歌浏览器 开启极速安全的 上网之旅。对此有专业解读
Мощный удар Израиля по Ирану попал на видео09:41
其实整体体验下来，尽管官方博客将 Nano Banana 2（Gemini 3.1 Flash Image）吹的天花乱坠，但实际体感中，生成的质量效果和速度并未得到肉眼可见的提升，甚至在部分场景中还不及前代模型。