请问HN：你们最喜欢的提示语是什么，以提高大型语言模型（LLM）的输出质量？
我经常使用Claude Code和GPT 5.5，发现它们在某些方面非常有用，但也常常陷入一些常见的性能低下的问题。例如，写作表现——也许我对它们最大的困扰就是写作风格——包括一些明显的风格习惯（如破折号、"这不是x而是y"），一些不太明显的风格习惯（如“诚实的框架”、“应得的XYZ”），晦涩且新造的行话，过度缩写，以及用箭头结构替代句子（这 -> 那 -> 另一件事），对受众对分析的熟悉程度理解不足，夸大的框架，以及以一种有动机的直观方式解释事物。
另一个常见的陷阱是过度设计分析解决方案，或者在我指出之前未能考虑到限制或常见的失败模式。
我很想听听你们遇到的其他失败模式，以及你们发现的有助于提高这些工具性能和可用输出的提示（系统提示或其他）、技能和其他技巧。
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I use Claude Code a lot and GPT 5.5 as well, and find that they are simultaneously extremely useful and also fall into common poor-performance basins. For example, writing performance -- perhaps my biggest issue with them is writing style -- such as well documented stylistic tics (em-dash, it's not x it's y), less commented stylistic tics ("the honest framing", "the earned XYZ"), cryptic and coined jargon, overabbreviation and sentences replaces by arrow constructions (this -> that -> this other thing), poor understanding of the audience's familiarity with the analysis, grandiose framings, as well as explaining things in a motivated intuitive way.<p>Another common trap is overengineering analytical solutions, or failing to consider limitations or common failure modes unless I point them out.<p>Would love to hear what other failure modes you've navigated, and see the prompts (system or otherwise), skills, and other techniques that you all have found helpful to get better performance and more usable output out of these tools.