提示词并非神秘的AI咒语, 而是可降低理解偏差的需求表达工具。本文起始于大模型工作逻辑, 剖析清晰提示词大幅度提升生成效果的缘由, 揭露AI产品设计过程当中场景入口、参数表单、追问机制设计的奥秘, 引领你掌握把模糊需求转变为精准指令的核心方法论。有不少人将提示词视作一种“AI咒语”, 觉得只要把控住某些固定的句式, 便能够让大模型产出更为优质的内容。然而从AI产品设计的层面来看, 提示词的根本性质并非技巧的堆砌, 而是助力大模型更精准地领会用户的需求。不论生成的是图片, 还是视频, 又或是音乐, 不管生成的是文案, 还是方案, 也不管生成的是页面, 甚至代码, 提示词的关键作用, 皆是降低模型对于用户意图的理解差错, 就此文章会从大模型的工作逻辑, 提示词与需求表达的关乎, 不同模型之间的能力区别之处, 以及AI产品设计之中提示词设计方式数个方面, 再度认知“为何提示词越条理清楚, 生成成效越理想”。一、为什么很多人写不好提示词越来越多的人当下开始动用 AI 工具, 然而好多人都会碰到一个问题:明明自己已经把需求告诉 AI 了为什么生成结果还是不对比如你想生成一张海报只输入帮我做一张高级感海报。最终模型有可能生成一张海报, 其背景是金色渐变样式, 带有大标题, 具备强光效, 呈现商务风格。然而, 你内心深处切实渴望的, 说不定是那种极简风格, 富有科技感, 呈现酸性设计的, 又或者是适配于品牌官网来运用的视觉主图。问题出在哪里不是模型完全不行而是“高级感”这个词本身太模糊。人类而言, “高级感”并非明确需求模型而言它更非可稳定执行的指令。它不知道你说的高级感到底是当用户未给出清晰上下文之际, 模型仅能依据概率去“猜”一个最为常见的答案。所以呀, 提示词要是写得不好, 从根本上来说, 并非是不懂得去写某一种固定的模板, 而在于没有将需求清清楚楚地表述出来。二、提示词的本质让模型更准确地理解需求站在看AI 产品设计的角度而言, 提示词不是那个“咒语”, 提示词是一种需求表达方式。用户向 AI 输入提示词本质上是在告诉模型这些信息越清楚模型对任务的理解就越稳定。举个例子, 同样是致使AI去生成一个App首页, 接下来的这两种表达成效全然不一样。第一种帮我设计一个电商 App 首页。第二种拜托帮着设计一个针对海外年轻消费者的跨境电商 App 的首页, 首页得有搜索栏, 得有商品分类入口, 得有限时折扣, 得有热门商品推荐, 也得有底部导航, 整体风格参照 Temu 和, 不过视觉上要更显干净, 更具年轻感, 还要更轻量化, 适宜用于中生成高保真移动端页面。更有效的是第二种提示词, 其之所以更有效, 并非源于它采用了所谓的“神奇句式”, 而是在于它补充了产品定位, 补充了目标用户, 补充了页面结构, 补充了设计风格, 补充了参考对象, 还补充了输出用途。这就是提示词的本质不是让模型变聪明而是让模型更少误解。三、大模型不是“读心术”它需要上下文有不少人, 对于大模型产生了一种误解, 那就是它们强有力, 所以理应能够理解我的真实想法, 自动地。但大模型并不是读心术。它没办法直接晓得你脑袋里的画面, 也不清楚你的业务目标, 审美偏好, 用户场景以及交付标准。它仅仅能够依照你所输入的信息来开展判断, 进而对接下来最有可能生成的内容作出预测。从底层的逻辑层面来讲, 大模型是处于依据上下文去开展概率预测的状态。你所输入的信息要是越完备, 那么它就越趋向于容易去对任务方向作出判断你所输入的信息要是越含混不清, 那么它的可以自由施展的空间就会越大。这同样是为何, 诸多由AI生成的结果, 会呈现出“看上去还挺好, 然而并非我所期望的”这般状况。虽然模型生成了确实一个合理答案, 可那个答案并非是你的真实需求。对 AI 产品设计者来说这一点非常重要。在传统产品设计中我们经常强调“需求澄清”在 AI 产品设计的范畴里, 提示词实则是一种全新的“需求澄清界面”。用户以这般状态去表达需求, 那个模型借如此路径来理解需求, 这系统凭这样方式引导用户补充需求, 这一切都会直接对最终生成质量产生影响。四、为什么提示词越清晰生成效果越好提示词越清晰生成效果越好核心原因有三个。1. 清晰提示词可以减少模型猜测空间模糊提示词会让模型有很大的发挥空间。比如做一个好看的页面。这里的“好看”没有标准。但如果你写成打造一个适配 AI 工具官网的首页, 其整体风格要具备简洁的特质, 还要带有科技感, 且是浅色背景, 首屏要突出产品价值, 突出核心功能, 突出使用流程以及CTA按钮, 视觉方面需参考 、AI 和 官网。模型的生成空间就被明显收窄了。它清楚, 这个页面属于AI工具官网, 并非电商首页, 也不是企业后台它明白, 你所需要的是简洁科技风格, 而非炫酷游戏风格它晓得, 页面结构理应依据价值表达以及转化目标来进行展开。这时生成结果自然更接近预期。2. 清晰提示词可以建立任务边界AI 很容易“过度发挥”。你吩咐它去撰写一个产品方案, 它或许会顺带帮你增添市场分析, 以及商业模式, 还有增长策略, 甚至包括竞品分析。这些内容不一定错但可能不是你当前阶段需要的。所以提示词中要明确任务边界任务边界越清楚AI 的输出越可控。3. 清晰提示词可以提高结果可评估性倘若你单单只是讲“写得高级一些”, 最终极难判定 AI 究竟有没有达成任务。但如果你明确要求那么生成结果是否合格就可以被评估。这对于 AI 产品设计非常关键。一个好的AI产品, 并非仅仅具备生成内容的能力, 于此之外, 还得让用户拥有判断所生成内容能否契合需求的能力, 原因便在于此。五、不同大模型之间的差异本质上也是理解能力的差异很多人比较大模型时喜欢看参数、速度、价格、上下文长度。但从真实使用体验看用户更关心的是这个模型到底懂不懂我处于面对模糊需求状况下时, 有些模型是很容易出现跑偏情况的。当用户仅仅表述了单单一句“做得高级一点”, 此时它极有可能是直接就堆满渐变, 堆满发光, 堆满金属材质, 还堆满大标题的。而能力更强的模型往往能更好地结合上下文理解用户意图。它能够判别你所讲的“高级感”究竟是应用于品牌官网, 还是产品界面, 又或是商业海报, 亦或是汇报PPT, 并且还会依据前后文去推测你更为倾向的输出结构, 以及设计风格, 还有业务目标。这也是为什么有些模型会让人感觉“更聪明”。确切来讲, 并非它具备了人的意识, 而是它针对上下文的建模本领更为突出, 对于用户意图的预估更为精准。也就是说它能更好地预测“你接下来真正想要什么”。这就是大模型能力差异在产品体验上的直接体现。六、站在 AI 产品设计角度提示词应该如何设计如果只是个人使用 AI提示词可以写得灵活一点。但是, 要是打算去设计一款面向用户的AI 产品, 那就不能够仅仅依靠用户自身会不会去写提示词。因为大部分用户并不擅长表达需求。这当口, AI产品设计者所要做的, 并非是把一个空白的输入框扔给用户, 而是借助产品机制去协助用户将需求讲明白。可以从四个方向入手。1. 用场景入口降低用户表达成本不要一上来就让用户面对一个空白对话框。可以提供明确的场景入口比如场景入口的作用是先帮用户确定任务类型。当用户做出了选取场景的行为之后, 系统能够凭借自身能力, 继而自动带入跟该场景相对应的提示词框架, 如此一来, 可将用户从完全没有任何基础开始进行描述的成本进行降低。2. 用参数表单补充关键信息有些信息不适合让用户自由输入而适合用表单或选择项引导。比如生成页面设计时可以让用户选择这些结构化信息本质上就是提示词的一部分。用户于界面之上进行参数的选择, 系统于后台将这些参数予以组织, 使其成为模型能够理解的。这就是 AI 产品设计中的“提示词产品化”。3. 用追问机制补全模糊需求当用户输入过于模糊时系统不应该直接生成而应该先追问。比如用户输入帮我设计一个包装页面。系统可以追问这个页面, 是用来进行包装打样下单的吗, 是促使包装创意生成的所在吗, 亦或是为包装作品展示而设的呢?目标用户是设计师、品牌方还是印刷厂业务员你希望页面偏工具型、营销型还是电商交易型这类追问可以显著提升最终生成质量。因为它把隐藏需求显性化了。4. 用输出模板提高结果稳定性AI 生成内容最大的问题之一是不稳定。同一个需求不同时间生成的结构可能完全不同。所以 AI 产品需要设计标准化输出模板。比如输出一篇产品分析文章可以固定为现象背景, 定义问题, 深入挖掘底层原因, 从产品设计的独特视角出发, 提出方法建议, 通过案例进行详细说明, 最终得出总结启发。对用户来讲, 那个模板能够把阅读成本给降低针对模型而言, 这个模板能够把输出漂移去减少。七、提示词能力本质上是产品经理的需求拆解能力很多人以为提示词能力是会写某些固定句式比如你是一个资深专家请一步一步思考。这类句式有一定作用但不是核心。真正重要的是你能不能把一个模糊想法拆成清晰任务。这和产品经理做需求分析非常像。用户说我想做一个更智能的包装设计平台。产品经理不能直接进入功能设计而要继续拆解同样写提示词也需要类似的拆解能力。一个好的提示词不只是描述“我要什么”还要描述因此, 提示词具备这种能力并非单纯的文字运用能力, 而是含有对需求进行分析的能力, 以及具备结构化表达理念这样的能力, 并且还是结果定义能力集合后的一种全面展现。八、对 AI 产品设计者的启发站在产品设计的视角去看, 提示词并非是那种孤立存在的输入框方面的问题, 而是属于 AI 产品体验设计范畴之内的核心问题。一个好的 AI 产品不应该要求所有用户都成为 专家。它应该通过产品设计帮助用户更容易地表达需求。具体来说有三个启发。1. 不要只设计“输入框”要设计“需求表达路径”一, 存在诸多AI产品, 其面临的问题在于, 二, 仅仅给予用户一个输入框, 三, 而后便期望用户能够个人独立自主地将自身需求清晰明确地表述出来。但现实是用户经常不知道该怎么说。所以产品设计要关注的是用户从模糊想法到清晰任务中间需要哪些引导这就需要场景入口、示例提示、参数选择、智能追问和结果预览。2. 不要只追求“生成能力”要追求“可控生成”AI 生成不是越自由越好。在商业场景中用户更需要的是稳定、可控、可复用。比如在包装设计的场景当中, 又如在页面设计的情形之下, 还比如在广告投放的状况里, 以及在内容生产的场景之中用户并非仅仅只想要一个“欣喜所得”, 而是期望所得契合品牌规范, 并且符合业务目标, 同时顺应生产限制, 还要遵循交付标准。所以提示词设计要和业务规则、设计规范、输出模板结合起来。3. 不要让用户适应模型要让产品帮助模型理解用户切实优质的 AI 产品, 并非使用户去研习一系列繁杂的提示词 , 而是将提示词的能力镶嵌于产品体验里头。用户看到的是简单入口、清晰表单和自然对话系统背后完成的是需求结构化、参数补全、 拼装和结果约束。这才是 AI 产品设计的价值所在。九、结语提示词不是咒语而是人与 AI 协作的需求语言提示词有着这般重要性, 并非缘由于它属于一套神神秘秘的技巧, 而是在于它对模型怎样去理解用户需求起到了决定作用。你表达得越清楚模型理解得越准确模型理解得越准确生成结果就越接近预期生成结果越稳定AI 产品的可用性就越强。从AI产品设计的角度去看, 提示词的能力, 它不单单是个人运用AI时所需的技巧, 并且更是未来阶段AI产品体验设计里极为重要的一个组成部分。它连接的是用户意图、模型能力和最终交付结果。所以提示词的本质不是“咒语”而是需求表达。未来, 真正具备价值的能力, 并非是谁收藏了更多的模板, 而是在于谁能够更为精准地界定问题, 将需求予以拆解, 并且把复杂的想法转变为模型能够理解以及执行的清晰指令。一句话总结把写得好的提示词究其原因, 并非在于你对“命令AI”更熟练, 而是在于你对自身需求有着更为清晰的认知和把握, 并且还更懂得运用恰当可行的方法, 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提示词的本质：不是“咒语”，而是 AI 产品设计中的需求表达能力
提示词并非神秘的AI咒语, 而是可降低理解偏差的需求表达工具。本文起始于大模型工作逻辑, 剖析清晰提示词大幅度提升生成效果的缘由, 揭露AI产品设计过程当中场景入口、参数表单、追问机制设计的奥秘, 引领你掌握把模糊需求转变为精准指令的核心方法论。有不少人将提示词视作一种“AI咒语”, 觉得只要把控住某些固定的句式, 便能够让大模型产出更为优质的内容。然而从AI产品设计的层面来看, 提示词的根本性质并非技巧的堆砌, 而是助力大模型更精准地领会用户的需求。不论生成的是图片, 还是视频, 又或是音乐, 不管生成的是文案, 还是方案, 也不管生成的是页面, 甚至代码, 提示词的关键作用, 皆是降低模型对于用户意图的理解差错, 就此文章会从大模型的工作逻辑, 提示词与需求表达的关乎, 不同模型之间的能力区别之处, 以及AI产品设计之中提示词设计方式数个方面, 再度认知“为何提示词越条理清楚, 生成成效越理想”。一、为什么很多人写不好提示词越来越多的人当下开始动用 AI 工具, 然而好多人都会碰到一个问题:明明自己已经把需求告诉 AI 了为什么生成结果还是不对比如你想生成一张海报只输入帮我做一张高级感海报。最终模型有可能生成一张海报, 其背景是金色渐变样式, 带有大标题, 具备强光效, 呈现商务风格。然而, 你内心深处切实渴望的, 说不定是那种极简风格, 富有科技感, 呈现酸性设计的, 又或者是适配于品牌官网来运用的视觉主图。问题出在哪里不是模型完全不行而是“高级感”这个词本身太模糊。人类而言, “高级感”并非明确需求模型而言它更非可稳定执行的指令。它不知道你说的高级感到底是当用户未给出清晰上下文之际, 模型仅能依据概率去“猜”一个最为常见的答案。所以呀, 提示词要是写得不好, 从根本上来说, 并非是不懂得去写某一种固定的模板, 而在于没有将需求清清楚楚地表述出来。二、提示词的本质让模型更准确地理解需求站在看AI 产品设计的角度而言, 提示词不是那个“咒语”, 提示词是一种需求表达方式。用户向 AI 输入提示词本质上是在告诉模型这些信息越清楚模型对任务的理解就越稳定。举个例子, 同样是致使AI去生成一个App首页, 接下来的这两种表达成效全然不一样。第一种帮我设计一个电商 App 首页。第二种拜托帮着设计一个针对海外年轻消费者的跨境电商 App 的首页, 首页得有搜索栏, 得有商品分类入口, 得有限时折扣, 得有热门商品推荐, 也得有底部导航, 整体风格参照 Temu 和, 不过视觉上要更显干净, 更具年轻感, 还要更轻量化, 适宜用于中生成高保真移动端页面。更有效的是第二种提示词, 其之所以更有效, 并非源于它采用了所谓的“神奇句式”, 而是在于它补充了产品定位, 补充了目标用户, 补充了页面结构, 补充了设计风格, 补充了参考对象, 还补充了输出用途。这就是提示词的本质不是让模型变聪明而是让模型更少误解。三、大模型不是“读心术”它需要上下文有不少人, 对于大模型产生了一种误解, 那就是它们强有力, 所以理应能够理解我的真实想法, 自动地。但大模型并不是读心术。它没办法直接晓得你脑袋里的画面, 也不清楚你的业务目标, 审美偏好, 用户场景以及交付标准。它仅仅能够依照你所输入的信息来开展判断, 进而对接下来最有可能生成的内容作出预测。从底层的逻辑层面来讲, 大模型是处于依据上下文去开展概率预测的状态。你所输入的信息要是越完备, 那么它就越趋向于容易去对任务方向作出判断你所输入的信息要是越含混不清, 那么它的可以自由施展的空间就会越大。这同样是为何, 诸多由AI生成的结果, 会呈现出“看上去还挺好, 然而并非我所期望的”这般状况。虽然模型生成了确实一个合理答案, 可那个答案并非是你的真实需求。对 AI 产品设计者来说这一点非常重要。在传统产品设计中我们经常强调“需求澄清”在 AI 产品设计的范畴里, 提示词实则是一种全新的“需求澄清界面”。用户以这般状态去表达需求, 那个模型借如此路径来理解需求, 这系统凭这样方式引导用户补充需求, 这一切都会直接对最终生成质量产生影响。四、为什么提示词越清晰生成效果越好提示词越清晰生成效果越好核心原因有三个。1. 清晰提示词可以减少模型猜测空间模糊提示词会让模型有很大的发挥空间。比如做一个好看的页面。这里的“好看”没有标准。但如果你写成打造一个适配 AI 工具官网的首页, 其整体风格要具备简洁的特质, 还要带有科技感, 且是浅色背景, 首屏要突出产品价值, 突出核心功能, 突出使用流程以及CTA按钮, 视觉方面需参考 、AI 和 官网。模型的生成空间就被明显收窄了。它清楚, 这个页面属于AI工具官网, 并非电商首页, 也不是企业后台它明白, 你所需要的是简洁科技风格, 而非炫酷游戏风格它晓得, 页面结构理应依据价值表达以及转化目标来进行展开。这时生成结果自然更接近预期。2. 清晰提示词可以建立任务边界AI 很容易“过度发挥”。你吩咐它去撰写一个产品方案, 它或许会顺带帮你增添市场分析, 以及商业模式, 还有增长策略, 甚至包括竞品分析。这些内容不一定错但可能不是你当前阶段需要的。所以提示词中要明确任务边界任务边界越清楚AI 的输出越可控。3. 清晰提示词可以提高结果可评估性倘若你单单只是讲“写得高级一些”, 最终极难判定 AI 究竟有没有达成任务。但如果你明确要求那么生成结果是否合格就可以被评估。这对于 AI 产品设计非常关键。一个好的AI产品, 并非仅仅具备生成内容的能力, 于此之外, 还得让用户拥有判断所生成内容能否契合需求的能力, 原因便在于此。五、不同大模型之间的差异本质上也是理解能力的差异很多人比较大模型时喜欢看参数、速度、价格、上下文长度。但从真实使用体验看用户更关心的是这个模型到底懂不懂我处于面对模糊需求状况下时, 有些模型是很容易出现跑偏情况的。当用户仅仅表述了单单一句“做得高级一点”, 此时它极有可能是直接就堆满渐变, 堆满发光, 堆满金属材质, 还堆满大标题的。而能力更强的模型往往能更好地结合上下文理解用户意图。它能够判别你所讲的“高级感”究竟是应用于品牌官网, 还是产品界面, 又或是商业海报, 亦或是汇报PPT, 并且还会依据前后文去推测你更为倾向的输出结构, 以及设计风格, 还有业务目标。这也是为什么有些模型会让人感觉“更聪明”。确切来讲, 并非它具备了人的意识, 而是它针对上下文的建模本领更为突出, 对于用户意图的预估更为精准。也就是说它能更好地预测“你接下来真正想要什么”。这就是大模型能力差异在产品体验上的直接体现。六、站在 AI 产品设计角度提示词应该如何设计如果只是个人使用 AI提示词可以写得灵活一点。但是, 要是打算去设计一款面向用户的AI 产品, 那就不能够仅仅依靠用户自身会不会去写提示词。因为大部分用户并不擅长表达需求。这当口, AI产品设计者所要做的, 并非是把一个空白的输入框扔给用户, 而是借助产品机制去协助用户将需求讲明白。可以从四个方向入手。1. 用场景入口降低用户表达成本不要一上来就让用户面对一个空白对话框。可以提供明确的场景入口比如场景入口的作用是先帮用户确定任务类型。当用户做出了选取场景的行为之后, 系统能够凭借自身能力, 继而自动带入跟该场景相对应的提示词框架, 如此一来, 可将用户从完全没有任何基础开始进行描述的成本进行降低。2. 用参数表单补充关键信息有些信息不适合让用户自由输入而适合用表单或选择项引导。比如生成页面设计时可以让用户选择这些结构化信息本质上就是提示词的一部分。用户于界面之上进行参数的选择, 系统于后台将这些参数予以组织, 使其成为模型能够理解的。这就是 AI 产品设计中的“提示词产品化”。3. 用追问机制补全模糊需求当用户输入过于模糊时系统不应该直接生成而应该先追问。比如用户输入帮我设计一个包装页面。系统可以追问这个页面, 是用来进行包装打样下单的吗, 是促使包装创意生成的所在吗, 亦或是为包装作品展示而设的呢?目标用户是设计师、品牌方还是印刷厂业务员你希望页面偏工具型、营销型还是电商交易型这类追问可以显著提升最终生成质量。因为它把隐藏需求显性化了。4. 用输出模板提高结果稳定性AI 生成内容最大的问题之一是不稳定。同一个需求不同时间生成的结构可能完全不同。所以 AI 产品需要设计标准化输出模板。比如输出一篇产品分析文章可以固定为现象背景, 定义问题, 深入挖掘底层原因, 从产品设计的独特视角出发, 提出方法建议, 通过案例进行详细说明, 最终得出总结启发。对用户来讲, 那个模板能够把阅读成本给降低针对模型而言, 这个模板能够把输出漂移去减少。七、提示词能力本质上是产品经理的需求拆解能力很多人以为提示词能力是会写某些固定句式比如你是一个资深专家请一步一步思考。这类句式有一定作用但不是核心。真正重要的是你能不能把一个模糊想法拆成清晰任务。这和产品经理做需求分析非常像。用户说我想做一个更智能的包装设计平台。产品经理不能直接进入功能设计而要继续拆解同样写提示词也需要类似的拆解能力。一个好的提示词不只是描述“我要什么”还要描述因此, 提示词具备这种能力并非单纯的文字运用能力, 而是含有对需求进行分析的能力, 以及具备结构化表达理念这样的能力, 并且还是结果定义能力集合后的一种全面展现。八、对 AI 产品设计者的启发站在产品设计的视角去看, 提示词并非是那种孤立存在的输入框方面的问题, 而是属于 AI 产品体验设计范畴之内的核心问题。一个好的 AI 产品不应该要求所有用户都成为 专家。它应该通过产品设计帮助用户更容易地表达需求。具体来说有三个启发。1. 不要只设计“输入框”要设计“需求表达路径”一, 存在诸多AI产品, 其面临的问题在于, 二, 仅仅给予用户一个输入框, 三, 而后便期望用户能够个人独立自主地将自身需求清晰明确地表述出来。但现实是用户经常不知道该怎么说。所以产品设计要关注的是用户从模糊想法到清晰任务中间需要哪些引导这就需要场景入口、示例提示、参数选择、智能追问和结果预览。2. 不要只追求“生成能力”要追求“可控生成”AI 生成不是越自由越好。在商业场景中用户更需要的是稳定、可控、可复用。比如在包装设计的场景当中, 又如在页面设计的情形之下, 还比如在广告投放的状况里, 以及在内容生产的场景之中用户并非仅仅只想要一个“欣喜所得”, 而是期望所得契合品牌规范, 并且符合业务目标, 同时顺应生产限制, 还要遵循交付标准。所以提示词设计要和业务规则、设计规范、输出模板结合起来。3. 不要让用户适应模型要让产品帮助模型理解用户切实优质的 AI 产品, 并非使用户去研习一系列繁杂的提示词 , 而是将提示词的能力镶嵌于产品体验里头。用户看到的是简单入口、清晰表单和自然对话系统背后完成的是需求结构化、参数补全、 拼装和结果约束。这才是 AI 产品设计的价值所在。九、结语提示词不是咒语而是人与 AI 协作的需求语言提示词有着这般重要性, 并非缘由于它属于一套神神秘秘的技巧, 而是在于它对模型怎样去理解用户需求起到了决定作用。你表达得越清楚模型理解得越准确模型理解得越准确生成结果就越接近预期生成结果越稳定AI 产品的可用性就越强。从AI产品设计的角度去看, 提示词的能力, 它不单单是个人运用AI时所需的技巧, 并且更是未来阶段AI产品体验设计里极为重要的一个组成部分。它连接的是用户意图、模型能力和最终交付结果。所以提示词的本质不是“咒语”而是需求表达。未来, 真正具备价值的能力, 并非是谁收藏了更多的模板, 而是在于谁能够更为精准地界定问题, 将需求予以拆解, 并且把复杂的想法转变为模型能够理解以及执行的清晰指令。一句话总结把写得好的提示词究其原因, 并非在于你对“命令AI”更熟练, 而是在于你对自身需求有着更为清晰的认知和把握, 并且还更懂得运用恰当可行的方法, 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