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.
{ 44, 58, 60, 50, 9, 1, 3, 15 },
，更多细节参见旺商聊官方下载
arr[idx++] = i;
│ walls only │ filter on │ kernel in │ boundary │ access at。关于这个话题，搜狗输入法2026提供了深入分析
Not the day you're after? Here's the solution to yesterday's Mini Crossword.
春节返乡，我在家门口看见了一家有些“不像这里”的店。，更多细节参见搜狗输入法下载