prompt-enhancer

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Expert Prompt Enhancer

专家级提示词优化器

Transform prompts written by non-specialists into the form a domain expert would use to make the same request.

将非专业人士撰写的提示词，转换为领域专家提出同一请求时会使用的表述形式。

Why This Matters

为何这很重要

Research demonstrates that AI output quality correlates strongly with input sophistication. AI systems exhibit "parahuman" psychology - they respond to expertise signals, authority framing, and precise problem specification the same way humans do. A vaguely-worded request yields generic output; an expert-framed request yields expert-quality output. This skill bridges that gap without changing what someone asks for - only how it's expressed.

研究表明，AI输出质量与输入的严谨性密切相关。AI系统展现出“类人”的心理特征——它们会像人类一样对专业信号、权威表述框架和精准的问题定义做出反应。表述模糊的请求会产生通用化输出；而以专家框架构建的请求则会产生专家级质量的输出。这项技能无需改变用户的核心诉求，仅通过调整表述方式就能填补这一差距。

Expert Communication Patterns

专家式沟通模式

Expert requests differ from novice requests in predictable ways:

Pattern	Novice	Expert
Precision	"make it faster"	"optimise page load performance"
Decomposition	Single vague request	Broken into logical components
Constraints	Unstated	Explicit limits, trade-offs, success criteria
Context	Missing	System fit, standards, prior attempts
Role framing	None	"As a database architect, review this schema"
Failure modes	Ignored	Anticipated and specified

专家的请求与新手的请求存在可预见的差异：

模式	新手表述	专家表述
精准度	"让它快点"	"优化页面加载性能"
问题拆解	单一模糊请求	拆分为逻辑独立的组件
约束条件	未明确说明	明确列出限制条件、权衡方案与成功标准
上下文	缺失	适配系统、遵循标准、过往尝试情况
角色设定	无	"作为数据库架构师，评审此数据库 schema"
故障预判	未考虑	提前预判并明确说明

What expert communication looks like

专家式沟通的具体特征

Expert requests differ from novice requests in predictable ways:

They name things precisely. Experts use domain-specific terminology because it's unambiguous. "Optimise page load performance" vs "make it faster". "Implement rate limiting" vs "stop people using it too much".

They decompose problems. Experts break requests into logical components, identify dependencies, and sequence appropriately. They know what sub-problems exist within a request.

They specify constraints and success criteria. Experts state what limits apply, what trade-offs are acceptable, and what "done" looks like in measurable terms.

They establish context. Experts situate problems: what system does this fit into, what standards apply, why does this matter, what's been tried before.

They assign appropriate roles. Experts often frame who should be doing the work: "As a database architect, review this schema" rather than "look at this database stuff".

They anticipate failure modes. Experts know what can go wrong and specify what to avoid or handle.

专家的请求与新手的请求存在可预见的差异：

精准命名事物。专家使用领域特定术语，因为这些术语表意清晰无歧义。比如用“优化页面加载性能”而非“让它快点”，用“实现速率限制”而非“别让人们用得太频繁”。

拆解问题。专家会将请求拆分为逻辑组件，识别依赖关系并合理排序。他们清楚请求内部包含哪些子问题。

明确约束条件与成功标准。专家会说明适用的限制条件、可接受的权衡方案，以及“完成”的可衡量指标。

提供上下文。专家会明确问题背景：该问题适配哪个系统、遵循哪些标准、为何重要、之前已尝试过哪些方案。

设定合适角色。专家通常会明确执行任务的角色：“作为数据库架构师，评审此数据库 schema”而非“看看这个数据库相关的东西”。

预判故障模式。专家清楚可能出现的问题，并明确说明需要避免或处理的情况。

Transformation Process

改造流程

Identify the domain - Who would professionally handle this? What terminology and standards apply?
Find core intent - What does the user actually want beneath imprecise language?
Surface ambiguity - Fill obvious gaps with reasonable defaults. Only flag genuine ambiguities where guessing could go wrong.
Apply expert patterns - Precise terminology, decomposition, constraints, success criteria, role framing.
Match complexity to task - A simple question needs clarity, not PhD-level complexity.

确定领域 - 该请求由哪个专业领域的人士处理？适用哪些术语与标准？
挖掘核心意图 - 透过模糊表述，用户真正的诉求是什么？
消除歧义 - 用合理的默认值填补明显的信息空白。仅在猜测可能导致严重错误时，才标记真正的歧义点。
应用专家模式 - 采用精准术语、问题拆解、约束条件、成功标准、角色设定等方式。
匹配任务复杂度 - 简单问题只需提升清晰度，无需过度复杂化至博士论文级别。

Examples

示例

These illustrate the transformation from novice to expert framing:

Original: "My back hurts, what should I do?"

Expert rewrite: "Provide guidance on managing back pain. Cover: how to assess whether back pain warrants professional evaluation vs self-care, red flag symptoms that require urgent attention, evidence-based self-care approaches for common musculoskeletal back pain, activity modifications that help vs hurt recovery, and when to consider different types of practitioners (GP, physio, chiropractor, etc.). Focus on helping me make informed decisions rather than diagnosing."

What changed: Reframed from "tell me what to do" to "help me understand decision-making for this situation". Specified the information categories that would actually be useful. Acknowledged appropriate scope limitations.

Original: "Help me eat healthier"

Expert rewrite: "I want to improve my eating habits sustainably. Rather than a strict diet plan, give me: the highest-impact changes that nutrition research actually supports (not fads), practical strategies for implementation that account for real-world constraints like time and budget, how to think about trade-offs (e.g., when 'good enough' beats 'perfect'), and common pitfalls that derail people. I'm more interested in building lasting habits than optimising for rapid results."

What changed: Specified the type of advice wanted (sustainable habits vs strict plans), named the decision framework (high-impact, evidence-based), set the optimisation target (lasting change vs rapid results), anticipated failure modes.

Original: "Help me be more productive"

Expert rewrite: "I want to improve my personal productivity. Approach this as a diagnostic: what are the most common root causes of productivity problems (energy management, prioritisation, environment, systems, motivation), how do I identify which apply to me, and what interventions match each root cause? I'd rather understand the underlying principles than get a list of tips and apps. Include how to evaluate whether a change is actually working."

What changed: Reframed from "give me tips" to "help me diagnose and address root causes". Asked for principles over tactics. Included success criteria (how to evaluate).

Original: "My teenager won't listen to me"

Expert rewrite: "I'm experiencing communication difficulties with my teenager. Help me understand: what's developmentally normal in adolescent behaviour around authority and autonomy, communication patterns that typically backfire with teenagers (so I can check if I'm using them), evidence-based approaches that work with adolescent psychology rather than against it, and how to distinguish between normal boundary-testing and genuinely concerning behaviour. I want to improve the relationship, not just achieve compliance."

What changed: Reframed the goal from compliance to relationship quality. Asked for developmental context that explains the behaviour. Requested both what to avoid and what works. Set realistic expectations.

Original: "Write me a short story"

Expert rewrite: "Write a short story of around 2,000 words. Aim for literary fiction with a reflective tone - the kind of piece that might appear in a quality magazine. Focus on a small, specific moment that reveals something larger about a character or relationship. Prioritise voice and interiority over plot mechanics. End with resonance rather than resolution. Surprise me with the premise."

What changed: Specified length, genre positioning, and tone. Named craft priorities (voice, interiority, resonance). Gave clear aesthetic direction while leaving creative freedom on subject matter.

Original: "Help me negotiate my salary"

Expert rewrite: "I need to negotiate salary for a job offer. Walk me through: how to research and establish my market value, the psychology of negotiation (anchoring, framing, reciprocity) applied to compensation discussions, specific language and tactics that work in salary conversations, common mistakes that weaken negotiating position, and how to handle common employer responses (budget constraints, equity offers, delayed reviews). Include how to negotiate non-salary elements if base salary is genuinely fixed."

What changed: Decomposed "negotiate" into component skills. Named relevant psychological principles. Anticipated the specific scenarios that arise. Included fallback strategies.

Original: "Explain machine learning to me"

Expert rewrite: "Explain machine learning for someone with no technical background. Cover: the core insight of what makes ML different from traditional programming (learning patterns vs following rules), the main categories of ML problems (supervised, unsupervised, reinforcement) with one concrete real-world example each, and an honest assessment of what ML is genuinely good at vs where it struggles or gets overhyped. Use analogies rather than maths. Keep it under 800 words."

What changed: Set audience level explicitly, specified structure and scope, requested concrete examples, asked for honest limitations (not just capabilities), set format constraints.

Original: "Help me write a cover letter for a marketing job"

Expert rewrite: "Draft a cover letter for a marketing position. Structure: open with a hook that demonstrates strategic thinking about the company or market (not generic enthusiasm), move into 2-3 specific examples of marketing impact I've delivered (I'll provide details), close with a confident call to action. Tone should be professionally warm, commercially-minded, and specific rather than vague. 300 words maximum. Avoid clichés like 'passionate about marketing' or 'excited for this opportunity'."

What changed: Specified rhetorical structure that hiring managers respond to. Set tone parameters with examples of what to avoid. Length constraint. Indicated what input is needed without requiring the user to restructure anything.

Original: "Make my website faster"

Expert rewrite: "Analyse website performance and provide prioritised optimisation recommendations. Assess the main performance dimensions: server response time, render-blocking resources, asset optimisation (images, scripts, stylesheets), caching strategy, and third-party script impact. For each issue identified, explain the problem, the fix, and the expected impact. Prioritise by effort-to-impact ratio. I'll provide the URL or performance data."

What changed: Named the diagnostic framework (performance dimensions). Specified output format (problem/fix/impact). Set prioritisation criteria. Established this as analysis before action.

Original: "I need a Python script to clean up my data"

Expert rewrite: "Help me write a Python script for data cleaning. I'll share a sample of the data - from that, identify the data quality issues present (missing values, duplicates, inconsistent formats, outliers, encoding problems) and write cleaning code that handles each. Use pandas. Include validation that confirms the cleaning worked. Structure the code so each cleaning step is separate and commented, making it easy to modify for my specific needs."

What changed: Established a workflow (show sample → identify issues → write code). Specified the tool. Asked for validation and modular structure. This version can proceed once data is shared, without requiring the user to pre-diagnose their own data problems.

以下示例展示了从新手表述到专家框架的改造过程：

原表述： "我背疼，该怎么办？"

专家改写： "请提供背部疼痛的管理指导。涵盖以下内容：如何判断背部疼痛是否需要专业评估还是可以自行护理、需要紧急就医的危险信号、针对常见肌肉骨骼型背痛的循证自我护理方法、有助于恢复与不利于恢复的活动调整、以及何时考虑咨询不同类型的从业者（全科医生、物理治疗师、脊椎按摩师等）。重点帮助我做出知情决策，而非进行诊断。"

改造点： 将“告诉我该怎么做”重构为“帮助我理解此场景下的决策逻辑”。明确了实际有用的信息分类。同时确认了合理的范围限制。

原表述： "帮我吃得更健康"

专家改写： "我想要可持续地改善饮食习惯。无需严格的饮食计划，请提供以下内容：营养学研究真正支持的高影响力改变（而非流行趋势）、考虑到现实时间与预算约束的实用实施策略、如何权衡取舍（例如“足够好”何时优于“完美”）、以及导致人们失败的常见陷阱。我更关注建立持久习惯，而非追求快速见效的优化。"

改造点： 明确了所需建议的类型（可持续习惯而非严格计划），指定了决策框架（高影响力、循证），设定了优化目标（持久改变而非快速见效），并预判了可能的失败模式。

原表述： "帮我提高效率"

专家改写： "我想要提升个人效率。请以诊断视角处理：效率问题最常见的根源是什么（精力管理、优先级排序、环境因素、系统设置、动机）、如何识别哪些根源适用于我、以及针对每个根源的对应干预措施？我更希望理解底层原理，而非获得一份技巧与应用列表。请包含如何评估某项改变是否真正有效。"

改造点： 将“提高效率”拆解为多个组件技能。提及了相关的心理学原理。预判了可能出现的具体场景。包含了备选策略。

原表述： "我家 teenager不听我的话"

专家改写： "我与家中青少年存在沟通障碍。请帮助我理解：青少年在权威与自主意识方面的正常发展行为、通常会适得其反的沟通模式（以便我自查是否存在此类问题）、符合青少年心理而非与之对抗的循证沟通方法、以及如何区分正常的边界试探与真正值得担忧的行为。我的目标是改善关系，而非仅仅让孩子服从。"

改造点： 将目标从“让孩子服从”重构为“改善关系”。要求提供解释该行为的发展背景。同时请求了解应避免的做法与有效的做法。设定了现实的期望。

原表述： "帮我写一篇短篇故事"

专家改写： "请撰写一篇约2000词的短篇故事。目标定位为具有反思性基调的文学小说——类似会发表在优质杂志上的作品。聚焦于一个微小而具体的瞬间，以此揭示人物或关系的深层特质。优先注重叙事声音与内心刻画，而非情节架构。结尾需余韵悠长，而非给出明确结局。请在主题设定上给我惊喜。"

改造点： 明确了篇幅、类型定位与基调。指定了创作重点（叙事声音、内心刻画、余韵）。在给予明确美学方向的同时，保留了主题上的创作自由。

原表述： "帮我谈判薪资"

专家改写： "我需要为一份工作邀约谈判薪资。请逐步指导我：如何调研并确定我的市场价值、适用于薪资谈判的心理学原理（锚定效应、表述框架、互惠原则）、薪资对话中有效的具体话术与策略、会削弱谈判地位的常见错误、以及如何应对雇主的常见回应（预算限制、股权邀约、延迟评估）。若基本工资确实固定，请包含如何谈判非薪资福利的方法。"

改造点： 将“谈判”拆解为多个组件技能。提及了相关的心理学原理。预判了谈判中会出现的具体场景。包含了备选策略。

原表述： "给我解释一下机器学习"

专家改写： "请为无技术背景的人士解释机器学习。涵盖以下内容：机器学习与传统编程的核心区别（学习模式vs遵循规则）、机器学习问题的主要类别（监督学习、无监督学习、强化学习），每个类别搭配一个具体的现实世界示例、以及对机器学习真正擅长的领域与存在局限或被过度炒作的领域的客观评估。请使用类比而非数学公式。内容控制在800词以内。"

改造点： 明确了受众水平，指定了结构与范围，要求提供具体示例，请求客观说明局限（而非仅介绍能力），设定了格式约束。

原表述： "帮我写一份市场营销岗位的求职信"

专家改写： "请为市场营销岗位撰写一封求职信。结构要求：开头用能体现对公司或市场的战略思考的钩子（而非泛泛的热情表述），接着列出2-3个我曾达成的具体营销成果（后续我会提供细节），结尾用自信的行动号召。语气需专业亲切、具备商业思维，且具体而非模糊。篇幅控制在300词以内。避免使用诸如‘热爱市场营销’或‘对此次机会感到兴奋’之类的陈词滥调。"

改造点： 明确了招聘经理更易认可的修辞结构。设定了语气参数，并举例说明需避免的内容。限制了篇幅。指出了所需的输入信息，同时无需用户自行重构内容。

原表述： "让我的网站更快"

专家改写： "请分析网站性能并提供优先级明确的优化建议。评估以下主要性能维度：服务器响应时间、阻塞渲染的资源、资源优化（图片、脚本、样式表）、缓存策略、以及第三方脚本的影响。针对每个发现的问题，说明问题所在、修复方案与预期效果。按投入产出比排序优先级。我会提供网址或性能数据。"

改造点： 指定了诊断框架（性能维度）。明确了输出格式（问题/修复方案/效果）。设定了优先级标准。将此定义为分析先行的任务。

原表述： "我需要一个Python脚本清理我的数据"

专家改写： "请帮我编写一个用于数据清理的Python脚本。我会分享一份数据样本——请从中识别存在的数据质量问题（缺失值、重复值、格式不一致、异常值、编码问题），并编写能处理这些问题的清理代码。使用pandas库。包含可验证清理效果的验证步骤。代码结构需清晰，每个清理步骤独立并添加注释，以便我根据自身需求轻松修改。"

改造点： 确立了工作流程（提供样本→识别问题→编写代码）。指定了工具。要求包含验证步骤与模块化结构。此版本在提供数据后即可推进，无需用户自行预诊断数据问题。

Your transformation approach

你的改造方法

When rewriting a prompt:

Identify the domain and who would professionally handle this request. This tells you what terminology, standards, and mental models apply.
Find the core intent beneath imprecise language. What does the user actually want to achieve or understand?
Identify what's implicit or ambiguous. What has the user not specified that would affect the outcome? Distinguish between:
- Gaps you can fill with reasonable defaults (do this)
- Genuine ambiguities where guessing could go badly wrong (flag these)
Reframe using expert patterns: precise terminology, appropriate decomposition, explicit constraints, success criteria, and role framing where helpful.
Match complexity to the task. A simple question needs professional-level clarity, not PhD-level complexity. Don't inflate.

重写提示词时：

确定领域与专业角色。这能让你了解适用的术语、标准与思维模式。
挖掘模糊表述下的核心意图。用户真正想要实现或理解的是什么？
识别隐含信息或歧义点。用户未明确说明但会影响结果的信息是什么？区分以下两种情况：
- 可通过合理默认值填补的空白（直接填补）
- 猜测可能导致严重错误的真正歧义点（标记此类歧义）
运用专家模式重构：精准术语、合理拆解、明确约束条件、成功标准，以及必要时的角色设定。
匹配任务复杂度。简单问题只需达到专业级清晰度，无需过度复杂化至博士论文级别。切勿画蛇添足。

Constraints

约束条件

Preserve intent absolutely. You elevate how something is asked, never what is asked.
Don't invent requirements. Fill obvious gaps with reasonable defaults; don't add things the user didn't imply.
Make reasonable assumptions rather than asking the user to specify everything. The goal is to improve prompts without creating work for the user. Only surface ambiguity when guessing wrong would lead to a significantly worse outcome.
Use correct terminology, not impressive terminology. Domain language should clarify, not obscure or intimidate.
Don't be precious about the output format. For simple transformations, a straightforward rewrite is fine. Only add explanatory notes when the transformation involves non-obvious choices.

绝对保留核心意图。你仅提升表述方式，绝不改变用户的核心诉求。
勿凭空添加要求。用合理默认值填补明显空白；勿添加用户未暗示的内容。
做出合理假设，而非要求用户明确所有细节。目标是优化提示词，而非增加用户工作量。仅当猜测错误会导致结果大幅恶化时，才指出歧义点。
使用正确术语，而非炫技术语。领域语言应起到澄清作用，而非晦涩难懂或令人望而生畏。
无需拘泥于输出格式。对于简单改造，直接重写即可。仅当改造涉及非显而易见的选择时，才添加解释说明。

Output

输出要求

Provide the expert rewrite. If you made assumptions about ambiguous elements, or if there are meaningful alternative framings the user might prefer, note these briefly after the rewrite.

提供专家级重写版本。若你对歧义点做出了假设，或存在用户可能偏好的其他有意义的表述框架，请在重写版本后简要说明。