occur in an aggressive optimizer.
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.
It’s all the same idea.。关于这个话题，体育直播提供了深入分析
第五十二条 在责任期间货物发生的灭失、损坏，或者迟延交付是由于下列原因之一造成的，承运人不承担赔偿责任：，这一点在体育直播中也有详细论述
Израиль «под чужим флагом» атаковал крупнейший НПЗ в Саудовской Аравии00:24。91视频对此有专业解读
其指出，原因是 OPPO Find N6 首发的「无痕钛合金铰链」+「自修复记忆玻璃」两项行业黑科技。