openai-prompt-engineer
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ChineseOpenAI Prompt Engineer
OpenAI 提示词工程师
A comprehensive skill for crafting, analyzing, and improving prompts for OpenAI's GPT-5 and other modern Large Language Models (LLMs), with focus on GPT-5-specific optimizations and universal prompting techniques.
这是一项针对OpenAI GPT-5及其他现代大语言模型(LLM)的提示词编写、分析与优化的综合技能,重点涵盖GPT-5专属优化方案与通用提示词技巧。
What This Skill Does
本技能的功能
Helps you create and optimize prompts using cutting-edge techniques:
- Generate new prompts - Build effective prompts from scratch
- Improve existing prompts - Enhance clarity, structure, and results
- Apply best practices - Use proven techniques for each model
- Optimize for specific models - GPT-5, Claude-specific strategies
- Implement advanced patterns - Chain-of-thought, few-shot, structured prompting
- Analyze prompt quality - Identify issues and suggest improvements
帮助你运用前沿技术创建并优化提示词:
- 生成新提示词 - 从零开始构建有效的提示词
- 优化现有提示词 - 提升清晰度、结构与输出效果
- 运用最佳实践 - 为每个模型采用经过验证的技巧
- 针对特定模型优化 - GPT-5、Claude专属策略
- 实现高级模式 - 运用Chain-of-thought、few-shot、结构化提示等模式
- 分析提示词质量 - 识别问题并提出改进建议
Why Prompt Engineering Matters
提示词工程的重要性
Without good prompts:
- Inconsistent or incorrect outputs
- Poor instruction following
- Wasted tokens and API costs
- Multiple attempts needed
- Unpredictable behavior
With optimized prompts:
- Accurate, consistent results
- Better instruction adherence
- Lower costs and latency
- First-try success
- Predictable, reliable outputs
缺乏优质提示词的后果:
- 输出结果不一致或错误
- 指令遵循度差
- 浪费Token与API成本
- 需要多次尝试
- 行为不可预测
使用优化后的提示词的优势:
- 结果准确、一致
- 指令遵循度更高
- 降低成本与延迟
- 一次尝试即可成功
- 输出可预测、可靠
Supported Models & Approaches
支持的模型与方法
GPT-5 (OpenAI)
GPT-5(OpenAI)
- Structured prompting (role + task + constraints)
- Reasoning effort calibration
- Agentic behavior control
- Verbosity management
- Prompt optimizer integration
- 结构化提示(角色+任务+约束)
- 推理难度校准
- Agent行为控制
- 冗长性管理
- 提示词优化器集成
Claude (Anthropic)
Claude(Anthropic)
- XML tag structuring
- Step-by-step thinking
- Clear, specific instructions
- Example-driven prompting
- Progressive disclosure
- XML标签结构化
- 分步思考
- 清晰、明确的指令
- 示例驱动的提示
- 渐进式披露
Universal Techniques
通用技巧
- Chain-of-thought prompting
- Few-shot learning
- Zero-shot prompting
- Self-consistency
- Role-based prompting
- Chain-of-thought提示
- Few-shot学习
- Zero-shot提示
- 自一致性
- 基于角色的提示
Core Prompting Principles
核心提示词原则
1. Be Clear and Specific
1. 清晰明确
Bad: "Write about AI"
Good: "Write a 500-word technical article explaining transformer architecture for software engineers with 2-3 years of experience. Include code examples in Python and focus on practical implementation."
反面示例: "写关于AI的内容"
正面示例: "为拥有2-3年经验的软件工程师撰写一篇500字的技术文章,解释Transformer架构。包含Python代码示例,并聚焦于实际实现。"
2. Provide Structure
2. 提供结构
Use clear formatting to organize instructions:
Role: You are a senior Python developer
Task: Review this code for security vulnerabilities
Constraints:
- Focus on OWASP Top 10
- Provide specific line numbers
- Suggest fixes with code examples
Output format: Markdown with severity ratings使用清晰的格式组织指令:
Role: 你是资深Python开发者
Task: 审查这段代码的安全漏洞
Constraints:
- 聚焦OWASP Top 10
- 提供具体行号
- 附带代码示例建议修复方案
Output format: 带有严重等级评分的Markdown格式3. Use Examples (Few-Shot)
3. 使用示例(Few-shot)
Show the model what you want:
Input: "User clicked login"
Output: "USER_LOGIN_CLICKED"
Input: "Payment processed successfully"
Output: "PAYMENT_PROCESSED_SUCCESS"
Input: "Email verification failed"
Output: [Your turn]向模型展示你想要的效果:
Input: "用户点击了登录"
Output: "USER_LOGIN_CLICKED"
Input: "支付处理成功"
Output: "PAYMENT_PROCESSED_SUCCESS"
Input: "邮箱验证失败"
Output: [请你完成]4. Enable Reasoning
4. 启用推理
Add phrases like:
- "Think step-by-step"
- "Let's break this down"
- "First, analyze... then..."
- "Show your reasoning"
添加如下表述:
- "分步思考"
- "让我们逐步分解"
- "首先,分析...然后..."
- "展示你的推理过程"
5. Define Output Format
5. 定义输出格式
Specify exactly how you want the response:
xml
<output_format>
<summary>One sentence overview</summary>
<details>
<point>Key finding 1</point>
<point>Key finding 2</point>
</details>
<recommendation>Specific action to take</recommendation>
</output_format>明确指定你想要的响应格式:
xml
<output_format>
<summary>一句话概述</summary>
<details>
<point>关键发现1</point>
<point>关键发现2</point>
</details>
<recommendation>具体行动建议</recommendation>
</output_format>Prompt Engineering Workflow
提示词工程工作流
1. Define Your Goal
1. 定义目标
- What task are you solving?
- What's the ideal output?
- Who's the audience?
- What model will you use?
- 你要解决什么任务?
- 理想的输出是什么?
- 受众是谁?
- 你将使用哪个模型?
2. Choose Your Technique
2. 选择合适的技巧
- Simple task? → Direct instruction
- Complex reasoning? → Chain-of-thought
- Pattern matching? → Few-shot examples
- Need consistency? → Structured format + examples
- 简单任务? → 直接指令
- 复杂推理? → Chain-of-thought
- 模式匹配? → Few-shot示例
- 需要一致性? → 结构化格式+示例
3. Build Your Prompt
3. 构建提示词
Use this template:
[ROLE/CONTEXT]
You are [specific role with relevant expertise]
[TASK]
[Clear, specific task description]
[CONSTRAINTS]
- [Limitation 1]
- [Limitation 2]
[FORMAT]
Output should be [exact format specification]
[EXAMPLES - if using few-shot]
[Example 1]
[Example 2]
[THINK STEP-BY-STEP - if complex reasoning]
Before answering, [thinking instruction]使用以下模板:
[ROLE/CONTEXT]
你是[具备相关专业知识的特定角色]
[TASK]
[清晰、具体的任务描述]
[CONSTRAINTS]
- [限制条件1]
- [限制条件2]
[FORMAT]
输出应为[精确的格式说明]
[EXAMPLES - 如使用few-shot]
[示例1]
[示例2]
[THINK STEP-BY-STEP - 如处理复杂推理]
在回答前,[思考指令]4. Test and Iterate
4. 测试与迭代
- Run the prompt
- Analyze output quality
- Identify issues
- Refine and retry
- Document what works
- 运行提示词
- 分析输出质量
- 识别问题
- 优化并重试
- 记录有效的方案
Advanced Techniques
高级技巧
Chain-of-Thought (CoT) Prompting
Chain-of-Thought (CoT) 提示
When to use: Complex reasoning, math, multi-step problems
How it works: Ask the model to show intermediate steps
Example:
Problem: A store has 15 apples. They sell 60% in the morning and
half of what's left in the afternoon. How many remain?
Please solve this step-by-step:
1. Calculate morning sales
2. Calculate remaining after morning
3. Calculate afternoon sales
4. Calculate final remainingResult: More accurate answers through explicit reasoning
适用场景: 复杂推理、数学问题、多步骤任务
工作原理: 要求模型展示中间步骤
示例:
问题:一家商店有15个苹果。上午卖出60%,下午卖出剩余的一半。最后还剩多少个?
请分步解决:
1. 计算上午卖出的数量
2. 计算上午剩余的数量
3. 计算下午卖出的数量
4. 计算最终剩余的数量效果: 通过显式推理获得更准确的答案
Few-Shot Prompting
Few-Shot 提示
When to use: Pattern matching, classification, style transfer
How it works: Provide 2-5 examples, then the actual task
Example:
Convert casual text to professional business tone:
Input: "Hey! Thanks for reaching out. Let's chat soon!"
Output: "Thank you for your message. I look forward to our conversation."
Input: "That's a great idea! I'm totally on board with this."
Output: "I appreciate your suggestion and fully support this initiative."
Input: "Sounds good, catch you later!"
Output: [Model completes]适用场景: 模式匹配、分类、风格转换
工作原理: 提供2-5个示例,然后给出实际任务
示例:
将非正式文本转换为专业商务语气:
输入:"嘿!感谢联系。我们尽快聊!"
输出:"感谢你的消息,我期待与你交流。"
输入:"这主意太棒了!我完全支持。"
输出:"我欣赏你的建议,并全力支持这项计划。"
输入:"听起来不错,回头见!"
输出:[模型完成]Zero-Shot Chain-of-Thought
Zero-Shot Chain-of-Thought
When to use: Complex problems without examples
How it works: Simply add "Let's think step by step"
Example:
Question: What are the security implications of storing JWTs
in localStorage?
Let's think step by step:Magic phrase: "Let's think step by step" → dramatically improves reasoning
适用场景: 无示例的复杂问题
工作原理: 只需添加“让我们分步思考”
示例:
问题:在localStorage中存储JWT有哪些安全隐患?
让我们分步思考:神奇表述: "让我们分步思考" → 大幅提升推理能力
Structured Output with XML
带XML的结构化输出
When to use: Working with Claude or need parsed output
Example:
Analyze this code for issues. Structure your response as:
<analysis>
<security_issues>
<issue severity="high|medium|low">
<description>What's wrong</description>
<location>File and line number</location>
<fix>How to fix it</fix>
</issue>
</security_issues>
<performance_issues>
<!-- Same structure -->
</performance_issues>
<best_practices>
<suggestion>Improvement suggestion</suggestion>
</best_practices>
</analysis>适用场景: 使用Claude或需要可解析的输出
示例:
分析这段代码的问题。按以下结构输出:
<analysis>
<security_issues>
<issue severity="high|medium|low">
<description>问题内容</description>
<location>文件与行号</location>
<fix>修复方法</fix>
</issue>
</security_issues>
<performance_issues>
<!-- 相同结构 -->
</performance_issues>
<best_practices>
<suggestion>改进建议</suggestion>
</best_practices>
</analysis>Progressive Disclosure
渐进式披露
When to use: Large context, multi-step workflows
How it works: Break tasks into stages, only request what's needed now
Example:
Stage 1: "Analyze this codebase structure and list the main components"
[Get response]
Stage 2: "Now, for the authentication component you identified,
show me the security review"
[Get response]
Stage 3: "Based on that review, generate fixes for the high-severity issues"适用场景: 大上下文、多步骤工作流
工作原理: 将任务拆分为多个阶段,仅请求当前所需的内容
示例:
阶段1:"分析这个代码库的结构,并列出主要组件"
[获取响应]
阶段2:"现在,针对你识别出的认证组件,展示安全审查结果"
[获取响应]
阶段3:"基于该审查结果,为高严重等级的问题生成修复方案"Model-Specific Best Practices
模型专属最佳实践
GPT-5 Optimization
GPT-5优化
Structured Prompting:
ROLE: Senior TypeScript Developer
TASK: Implement user authentication service
CONSTRAINTS:
- Use JWT with refresh tokens
- TypeScript with strict mode
- Include comprehensive error handling
- Follow SOLID principles
OUTPUT: Complete TypeScript class with JSDoc comments
REASONING_EFFORT: high (for complex business logic)Control Agentic Behavior:
"Implement this feature step-by-step, asking for confirmation
before each major decision"
OR
"Complete this task end-to-end without asking for guidance.
Persist until fully handled."Manage Verbosity:
"Provide a concise implementation (under 100 lines) focusing
only on core functionality"结构化提示:
ROLE: 资深TypeScript开发者
TASK: 实现用户认证服务
CONSTRAINTS:
- 使用带刷新令牌的JWT
- 启用严格模式的TypeScript
- 包含全面的错误处理
- 遵循SOLID原则
OUTPUT: 带有JSDoc注释的完整TypeScript类
REASONING_EFFORT: high(针对复杂业务逻辑)控制Agent行为:
"分步实现此功能,在每个重大决策前请求确认"
或
"端到端完成此任务,无需请求指导。持续处理直至完成。"管理冗长性:
"提供简洁的实现(少于100行),仅聚焦核心功能"Claude Optimization
Claude优化
Use XML Tags:
<instruction>
Review this pull request for security issues
</instruction>
<code>
[Code to review]
</code>
<focus_areas>
- SQL injection vulnerabilities
- XSS attack vectors
- Authentication bypasses
- Data exposure risks
</focus_areas>
<output_format>
For each issue found, provide:
1. Severity (Critical/High/Medium/Low)
2. Location
3. Explanation
4. Fix recommendation
</output_format>Step-by-Step Thinking:
Think through this architecture decision step by step:
1. First, identify the requirements
2. Then, list possible approaches
3. Evaluate trade-offs for each
4. Make a recommendation with reasoningClear Specificity:
BAD: "Make the response professional"
GOOD: "Use formal business language, avoid contractions,
address the user as 'you', keep sentences under 20 words"使用XML标签:
<instruction>
审查此拉取请求的安全问题
</instruction>
<code>
[待审查代码]
</code>
<focus_areas>
- SQL注入漏洞
- XSS攻击向量
- 认证绕过
- 数据泄露风险
</focus_areas>
<output_format>
针对每个发现的问题,提供:
1. 严重等级(Critical/High/Medium/Low)
2. 位置
3. 解释
4. 修复建议
</output_format>分步思考:
分步思考这个架构决策:
1. 首先,识别需求
2. 然后,列出可能的方案
3. 评估每个方案的权衡
4. 给出带有推理过程的建议清晰明确:
反面示例:"让回复更专业"
正面示例:"使用正式商务语言,避免缩写,以'您'称呼用户,句子长度控制在20词以内"Prompt Improvement Checklist
提示词优化检查清单
Use this checklist to improve any prompt:
- Clear role defined - Is the AI's expertise specified?
- Specific task - Is it unambiguous what to do?
- Constraints listed - Are limitations clear?
- Format specified - Is output structure defined?
- Examples provided - Do you show what you want (if needed)?
- Reasoning enabled - Do you ask for step-by-step thinking (if complex)?
- Context included - Does the AI have necessary background?
- Edge cases covered - Are exceptions handled?
- Length specified - Is output length clear?
- Tone/style defined - Is the desired voice specified?
使用此清单优化任何提示词:
- 明确角色 - 是否指定了AI的专业领域?
- 具体任务 - 任务是否明确无歧义?
- 列出约束 - 限制条件是否清晰?
- 指定格式 - 是否定义了输出结构?
- 提供示例 - 是否展示了预期效果(如需要)?
- 启用推理 - 是否要求分步思考(如处理复杂任务)?
- 包含上下文 - AI是否拥有必要的背景信息?
- 覆盖边缘情况 - 是否处理了异常情况?
- 指定长度 - 输出长度是否明确?
- 定义语气/风格 - 是否指定了所需的语气?
Common Prompt Problems & Fixes
常见提示词问题与修复方案
Problem: Vague Instructions
问题:模糊的指令
Before:
"Write some code for user authentication"After:
"Write a TypeScript class called AuthService that:
- Accepts email/password credentials
- Validates against a User repository
- Returns a JWT token on success
- Throws AuthenticationError on failure
- Includes comprehensive JSDoc comments
- Follows dependency injection pattern"修复前:
"写一些用户认证的代码"修复后:
"编写一个名为AuthService的TypeScript类,要求:
- 接受邮箱/密码凭证
- 与User仓库进行验证
- 成功时返回JWT令牌
- 失败时抛出AuthenticationError
- 包含全面的JSDoc注释
- 遵循依赖注入模式"Problem: No Examples (When Needed)
问题:缺少必要的示例
Before:
"Convert these variable names to camelCase"After:
"Convert these variable names to camelCase:
user_name → userName
total_count → totalCount
is_active → isActive
Now convert:
order_status →
created_at →
max_retry_count →"修复前:
"将这些变量名转换为驼峰式"修复后:
"将这些变量名转换为驼峰式:
user_name → userName
total_count → totalCount
is_active → isActive
现在转换:
order_status →
created_at →
max_retry_count →"Problem: Missing Output Format
问题:缺少输出格式
Before:
"Analyze this code for problems"After:
"Analyze this code and output in this format:修复前:
"分析这段代码的问题"修复后:
"分析这段代码,并按以下格式输出:Security Issues
安全问题
Performance Issues
性能问题
Code Quality
代码质量
Recommendations
建议
- [Priority 1 fix]
- [Priority 2 fix]"
undefined- [优先级1修复方案]
- [优先级2修复方案]"
undefinedProblem: Too Complex (Single Shot)
问题:单提示词过于复杂
Before:
"Build a complete e-commerce backend with authentication,
payments, inventory, and shipping"After (Progressive):
"Let's build this in stages:
Stage 1: Design the authentication system architecture
[Get response, review]
Stage 2: Implement the auth service
[Get response, review]
Stage 3: Add payment processing
[Continue...]"修复前:
"构建一个完整的电商后端,包含认证、支付、库存与物流功能"修复后(渐进式):
"我们分阶段构建:
阶段1:设计认证系统架构
[获取响应,审查]
阶段2:实现认证服务
[获取响应,审查]
阶段3:添加支付处理
[继续...]"Using This Skill
如何使用本技能
Generate a New Prompt
生成新提示词
Ask:
"Using the prompt-engineer skill, create a prompt for:
[Describe your task and requirements]"You'll get:
- Structured prompt template
- Recommended techniques
- Example few-shots if applicable
- Model-specific optimizations
提问:
"使用prompt-engineer技能,为以下需求创建提示词:
[描述你的任务与要求]"你将获得:
- 结构化提示词模板
- 推荐的技巧
- 适用的few-shot示例
- 模型专属优化方案
Improve an Existing Prompt
优化现有提示词
Ask:
"Using the prompt-engineer skill, improve this prompt:
[Your current prompt]
Goal: [What you want to achieve]
Model: [GPT-5 / Claude / Other]"You'll get:
- Analysis of current issues
- Improved version
- Explanation of changes
- Expected improvement in results
提问:
"使用prompt-engineer技能,优化以下提示词:
[你当前的提示词]
目标:[你想要实现的效果]
模型:[GPT-5 / Claude / 其他]"你将获得:
- 当前问题分析
- 优化后的版本
- 变更说明
- 预期的效果提升
Analyze Prompt Quality
分析提示词质量
Ask:
"Using the prompt-engineer skill, analyze this prompt:
[Your prompt]"You'll get:
- Quality score
- Identified weaknesses
- Specific improvement suggestions
- Best practices violations
提问:
"使用prompt-engineer技能,分析以下提示词:
[你的提示词]"你将获得:
- 质量评分
- 识别出的弱点
- 具体的改进建议
- 违反的最佳实践
Real-World Examples
真实场景示例
Example 1: Code Review Prompt
示例1:代码审查提示词
Task: Get thorough, consistent code reviews
Optimized Prompt:
ROLE: Senior Software Engineer conducting PR review
REVIEW THIS CODE:
[code block]
REVIEW CRITERIA:
1. Security vulnerabilities (OWASP Top 10)
2. Performance issues
3. Code quality and readability
4. Best practices compliance
5. Test coverage gaps
OUTPUT FORMAT:
For each issue found:
- Severity: [Critical/High/Medium/Low]
- Category: [Security/Performance/Quality/Testing]
- Location: [File:Line]
- Issue: [Clear description]
- Impact: [Why this matters]
- Fix: [Specific code recommendation]
At the end, provide:
- Overall assessment (Approve/Request Changes/Comment)
- Summary of critical items that must be fixed任务: 获取全面、一致的代码审查结果
优化后的提示词:
ROLE: 进行PR审查的资深软件工程师
审查以下代码:
[代码块]
审查标准:
1. 安全漏洞(OWASP Top 10)
2. 性能问题
3. 代码质量与可读性
4. 最佳实践合规性
5. 测试覆盖缺口
输出格式:
针对每个发现的问题:
- 严重等级:[Critical/High/Medium/Low]
- 分类:[Security/Performance/Quality/Testing]
- 位置:[文件:行号]
- 问题:[清晰描述]
- 影响:[为什么这很重要]
- 修复:[具体代码建议]
最后,提供:
- 整体评估(Approve/Request Changes/Comment)
- 必须修复的关键问题摘要Example 2: Technical Documentation
示例2:技术文档
Task: Generate clear API documentation
Optimized Prompt:
ROLE: Technical writer with API documentation expertise
TASK: Generate API documentation for this endpoint
ENDPOINT DETAILS:
[code/specs]
DOCUMENTATION REQUIREMENTS:
- Target audience: Junior to mid-level developers
- Include curl and JavaScript examples
- Explain all parameters clearly
- Show example responses with descriptions
- Include common error cases
- Add troubleshooting section
FORMAT:任务: 生成清晰的API文档
优化后的提示词:
ROLE: 具备API文档专业知识的技术作家
任务:为以下端点生成API文档
端点详情:
[代码/规范]
文档要求:
- 目标受众:初级到中级开发者
- 包含curl与JavaScript示例
- 清晰解释所有参数
- 展示带描述的示例响应
- 包含常见错误案例
- 添加故障排除部分
格式:[Endpoint Name]
[端点名称]
Overview
概述
[One paragraph description]
[一段描述]
Endpoint
端点
[HTTP METHOD] /path[HTTP方法] /路径Parameters
参数
| Name | Type | Required | Description |
|---|
| 名称 | 类型 | 是否必填 | 描述 |
|---|
Request Example
请求示例
bash
[curl example]bash
[curl示例]Response
响应
Success (200)
成功(200)
json
[example with inline comments]json
[带内联注释的示例]Errors
错误
- 400: [Description and fix]
- 401: [Description and fix]
- 400: [描述与修复方案]
- 401: [描述与修复方案]
Common Issues
常见问题
[Troubleshooting guide]
undefined[故障排除指南]
undefinedExample 3: Data Analysis
示例3:数据分析
Task: Analyze data and provide insights
Optimized Prompt:
ROLE: Data analyst with expertise in business metrics
DATA:
[dataset]
ANALYSIS REQUEST:
Analyze this data step-by-step:
1. FIRST: Identify key metrics and trends
2. THEN: Calculate:
- Growth rate (month-over-month)
- Average values
- Anomalies or outliers
3. NEXT: Draw business insights
4. FINALLY: Provide actionable recommendations
OUTPUT FORMAT:任务: 分析数据并提供洞察
优化后的提示词:
ROLE: 具备业务指标专业知识的数据分析师
数据:
[数据集]
分析请求:
分步分析此数据:
1. 首先:识别关键指标与趋势
2. 然后:计算:
- 环比增长率
- 平均值
- 异常值
3. 接下来:得出业务洞察
4. 最后:提供可执行的建议
输出格式:Executive Summary
执行摘要
[2-3 sentences]
[2-3句话]
Key Metrics
关键指标
| Metric | Value | Change | Trend |
| 指标 | 数值 | 变化 | 趋势 |
Insights
洞察
- [Insight with supporting data]
- [Insight with supporting data]
- [带数据支持的洞察]
- [带数据支持的洞察]
Recommendations
建议
- [Action]: [Expected impact]
- [Action]: [Expected impact]
Methodology
方法论
[Brief explanation of analysis approach]
undefined[分析方法的简要说明]
undefinedBest Practices Summary
最佳实践总结
DO ✅
建议✅
- Be specific - Exact requirements, not vague requests
- Use structure - Organize with clear sections
- Provide examples - Show what you want (few-shot)
- Request reasoning - "Think step-by-step" for complex tasks
- Define format - Specify exact output structure
- Test iteratively - Refine based on results
- Match to model - Use model-specific techniques
- Include context - Give necessary background
- Handle edge cases - Specify exception handling
- Set constraints - Define limitations clearly
- 明确具体 - 精确的需求,而非模糊的请求
- 使用结构 - 用清晰的章节组织内容
- 提供示例 - 展示预期效果(few-shot)
- 要求推理 - 处理复杂任务时使用“分步思考”
- 定义格式 - 指定精确的输出结构
- 迭代测试 - 根据结果优化
- 匹配模型 - 使用模型专属技巧
- 包含上下文 - 提供必要的背景信息
- 处理边缘情况 - 指定异常处理方式
- 设置约束 - 清晰定义限制条件
DON'T ❌
避免❌
- Be vague - "Write something about X"
- Skip examples - When patterns need to be matched
- Assume format - Model will choose unpredictably
- Overload single prompt - Break complex tasks into stages
- Ignore model differences - GPT-5 and Claude need different approaches
- Give up too soon - Iterate on prompts
- Mix instructions - Keep separate concerns separate
- Forget constraints - Specify ALL requirements
- Use ambiguous terms - "Good", "professional", "better" without definition
- Skip testing - Always validate outputs
- 模糊不清 - “写一些关于X的内容”
- 缺少示例 - 当需要匹配模式时
- 假设格式 - 模型会选择不可预测的格式
- 单提示词过载 - 将复杂任务拆分为多个阶段
- 忽略模型差异 - GPT-5与Claude需要不同的方法
- 过早放弃 - 迭代优化提示词
- 混合指令 - 分离不同的关注点
- 忘记约束 - 指定所有需求
- 使用模糊术语 - 未定义的“好”“专业”“更好”
- 跳过测试 - 始终验证输出
Quick Reference
快速参考
Prompt Template (Universal)
通用提示词模板
[ROLE]
You are [specific expertise]
[CONTEXT]
[Background information]
[TASK]
[Clear, specific task]
[CONSTRAINTS]
- [Limit 1]
- [Limit 2]
[FORMAT]
[Exact output structure]
[EXAMPLES - Optional]
[2-3 examples]
[REASONING - Optional]
Think through this step-by-step:
[Thinking guidance][ROLE]
你是[特定专业领域]
[CONTEXT]
[背景信息]
[TASK]
[清晰、具体的任务]
[CONSTRAINTS]
- [限制1]
- [限制2]
[FORMAT]
[精确的输出结构]
[EXAMPLES - 可选]
[2-3个示例]
[REASONING - 可选]
分步思考:
[思考指导]When to Use Each Technique
各技巧适用场景
| Technique | Best For | Example Use Case |
|---|---|---|
| Chain-of-Thought | Complex reasoning | Math, logic puzzles, multi-step analysis |
| Few-Shot | Pattern matching | Classification, style transfer, formatting |
| Zero-Shot | Simple, clear tasks | Direct questions, basic transformations |
| Structured (XML) | Parsed output | Data extraction, API responses |
| Progressive Disclosure | Large tasks | Full implementations, research |
| Role-Based | Expert knowledge | Code review, architecture decisions |
| 技巧 | 最佳适用场景 | 示例用例 |
|---|---|---|
| Chain-of-Thought | 复杂推理 | 数学、逻辑谜题、多步骤分析 |
| Few-Shot | 模式匹配 | 分类、风格转换、格式化 |
| Zero-Shot | 简单清晰的任务 | 直接问题、基础转换 |
| 结构化(XML) | 可解析输出 | 数据提取、API响应 |
| 渐进式披露 | 大型任务 | 完整实现、研究 |
| 基于角色 | 专业知识需求 | 代码审查、架构决策 |
Model Selection Guide
模型选择指南
Use GPT-5 when:
- Need strong reasoning
- Agentic behavior helpful
- Code generation focus
- Latest knowledge needed
Use Claude when:
- Very long context (100K+ tokens)
- Detailed instruction following
- Safety-critical applications
- Prefer XML structuring
选择GPT-5的场景:
- 需要强大的推理能力
- Agent行为有帮助
- 聚焦代码生成
- 需要最新知识
选择Claude的场景:
- 超长上下文(100K+ Token)
- 详细的指令遵循
- 安全关键型应用
- 偏好XML结构化
Resources
资源
All reference materials included:
- GPT-5 specific techniques and patterns
- Claude optimization strategies
- Advanced prompting patterns
- Optimization and improvement frameworks
包含所有参考资料:
- GPT-5专属技巧与模式
- Claude优化策略
- 高级提示词模式
- 优化与改进框架
Summary
总结
Effective prompt engineering:
- Saves time - Get right results faster
- Reduces costs - Fewer API calls needed
- Improves quality - More accurate, consistent outputs
- Enables complexity - Tackle harder problems
- Scales knowledge - Capture best practices
Use this skill to create prompts that:
- Are clear and specific
- Use proven techniques
- Match your model
- Get consistent results
- Achieve your goals
Remember: A well-crafted prompt is worth 10 poorly-attempted ones. Invest time upfront for better results.
有效的提示词工程:
- 节省时间 - 更快获得正确结果
- 降低成本 - 减少API调用次数
- 提升质量 - 输出更准确、一致
- 支持复杂任务 - 处理更难的问题
- 规模化知识 - 固化最佳实践
使用本技能创建以下提示词:
- 清晰明确
- 运用经过验证的技巧
- 匹配你的模型
- 获得一致的结果
- 实现你的目标
记住: 一个精心设计的提示词胜过10个粗糙的尝试。提前投入时间,获得更好的结果。