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.
I'm publishing this to start a conversation. What did I get right? What did I miss? Are there use cases that don't fit this model? What would a migration path for this approach look like? The goal is to gather feedback from developers who've felt the pain of Web streams and have opinions about what a better API should look like.。关于这个话题，WPS官方版本下载提供了深入分析
。业内人士推荐雷电模拟器官方版本下载作为进阶阅读
Overall, OpenAI Codex is a powerful tool that can help
Медведев вышел в финал турнира в Дубае17:59。safew官方版本下载对此有专业解读