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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.
。业内人士推荐体育直播作为进阶阅读
Трамп определил приоритетность Украины для США20:32。体育直播是该领域的重要参考
Unfolding the space with two lines removed (Bobs home) to a double doughnut.
I haven’t found this heuristic documented anywhere, but I’ve decided to put it here, because I’ve used it and I can bet I’m not the only one. This heuristic is a scaled down version of the “Service per team” pattern from Microservices.