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Prompt Engineering Patterns

Prompt Engineering 模式

Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
用于最大化LLM性能、可靠性和可控性的高级Prompt Engineering技术。

Core Capabilities

核心能力

1. Few-Shot Learning

1. Few-Shot Learning

Teach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity.
Example:
markdown
Extract key information from support tickets:

Input: "My login doesn't work and I keep getting error 403"
Output: {"issue": "authentication", "error_code": "403", "priority": "high"}

Input: "Feature request: add dark mode to settings"
Output: {"issue": "feature_request", "error_code": null, "priority": "low"}

Now process: "Can't upload files larger than 10MB, getting timeout"
通过展示示例而非解释规则来训练模型。包含2-5个能展示预期行为的输入输出对。当你需要一致的格式、特定的推理模式或处理边缘情况时使用。更多示例会提高准确性，但会消耗token——需根据任务复杂度进行平衡。
示例：
markdown
Extract key information from support tickets:

Input: "My login doesn't work and I keep getting error 403"
Output: {"issue": "authentication", "error_code": "403", "priority": "high"}

Input: "Feature request: add dark mode to settings"
Output: {"issue": "feature_request", "error_code": null, "priority": "low"}

Now process: "Can't upload files larger than 10MB, getting timeout"

2. Chain-of-Thought Prompting

2. Chain-of-Thought Prompting

Request step-by-step reasoning before the final answer. Add "Let's think step by step" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%.
Example:
markdown
Analyze this bug report and determine root cause.

Think step by step:

1. What is the expected behavior?
2. What is the actual behavior?
3. What changed recently that could cause this?
4. What components are involved?
5. What is the most likely root cause?

Bug: "Users can't save drafts after the cache update deployed yesterday"
要求在给出最终答案前进行逐步推理。添加“Let's think step by step”（零样本）或包含示例推理轨迹（少样本）。适用于需要多步骤逻辑、数学推理或需要验证模型思考过程的复杂问题。可将分析任务的准确性提高30-50%。
示例：
markdown
Analyze this bug report and determine root cause.

Think step by step:

1. What is the expected behavior?
2. What is the actual behavior?
3. What changed recently that could cause this?
4. What components are involved?
5. What is the most likely root cause?

Bug: "Users can't save drafts after the cache update deployed yesterday"

3. Prompt Optimization

3. Prompt Optimization

Systematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A/B testing to compare variations. Critical for production prompts where consistency and cost matter.
Example:
markdown
Version 1 (Simple): "Summarize this article"
→ Result: Inconsistent length, misses key points

Version 2 (Add constraints): "Summarize in 3 bullet points"
→ Result: Better structure, but still misses nuance

Version 3 (Add reasoning): "Identify the 3 main findings, then summarize each"
→ Result: Consistent, accurate, captures key information
通过测试和优化系统性地改进提示词。从简单版本开始，衡量性能（准确性、一致性、token消耗），然后迭代。在包含边缘情况的多样化输入上进行测试。使用A/B测试比较不同变体。这对于一致性和成本至关重要的生产环境提示词来说非常关键。
示例：
markdown
Version 1 (Simple): "Summarize this article"
→ Result: Inconsistent length, misses key points

Version 2 (Add constraints): "Summarize in 3 bullet points"
→ Result: Better structure, but still misses nuance

Version 3 (Add reasoning): "Identify the 3 main findings, then summarize each"
→ Result: Consistent, accurate, captures key information

4. Template Systems

4. 模板系统

Build reusable prompt structures with variables, conditional sections, and modular components. Use for multi-turn conversations, role-based interactions, or when the same pattern applies to different inputs. Reduces duplication and ensures consistency across similar tasks.
Example:
python
undefined
构建包含变量、条件部分和模块化组件的可复用提示词结构。适用于多轮对话、基于角色的交互，或同一模式适用于不同输入的场景。减少重复工作，确保相似任务的一致性。
示例：
python
undefined

Reusable code review template

Reusable code review template

template = """ Review this {language} code for {focus_area}.
Code: {code_block}
Provide feedback on: {checklist} """
template = """ Review this {language} code for {focus_area}.
Code: {code_block}
Provide feedback on: {checklist} """

Usage

Usage

prompt = template.format( language="Python", focus_area="security vulnerabilities", code_block=user_code, checklist="1. SQL injection\n2. XSS risks\n3. Authentication" )
undefined
prompt = template.format( language="Python", focus_area="security vulnerabilities", code_block=user_code, checklist="1. SQL injection\n2. XSS risks\n3. Authentication" )
undefined

5. System Prompt Design

5. System Prompt设计

Set global behavior and constraints that persist across the conversation. Define the model's role, expertise level, output format, and safety guidelines. Use system prompts for stable instructions that shouldn't change turn-to-turn, freeing up user message tokens for variable content.
Example:
markdown
System: You are a senior backend engineer specializing in API design.

Rules:

- Always consider scalability and performance
- Suggest RESTful patterns by default
- Flag security concerns immediately
- Provide code examples in Python
- Use early return pattern

Format responses as:

1. Analysis
2. Recommendation
3. Code example
4. Trade-offs
设置在整个对话中持续生效的全局行为和约束。定义模型的角色、专业水平、输出格式和安全准则。System Prompt适用于不应随对话轮次改变的稳定指令，从而将用户消息的token留给可变内容。
示例：
markdown
System: You are a senior backend engineer specializing in API design.

Rules:

- Always consider scalability and performance
- Suggest RESTful patterns by default
- Flag security concerns immediately
- Provide code examples in Python
- Use early return pattern

Format responses as:

1. Analysis
2. Recommendation
3. Code example
4. Trade-offs

Key Patterns

关键模式

Progressive Disclosure

Progressive Disclosure

Start with simple prompts, add complexity only when needed:
  1. Level 1: Direct instruction
    • "Summarize this article"
  2. Level 2: Add constraints
    • "Summarize this article in 3 bullet points, focusing on key findings"
  3. Level 3: Add reasoning
    • "Read this article, identify the main findings, then summarize in 3 bullet points"
  4. Level 4: Add examples
    • Include 2-3 example summaries with input-output pairs
从简单的提示词开始，仅在需要时增加复杂度：
  1. 级别1：直接指令
    • "Summarize this article"
  2. 级别2：添加约束
    • "Summarize this article in 3 bullet points, focusing on key findings"
  3. 级别3：添加推理要求
    • "Read this article, identify the main findings, then summarize in 3 bullet points"
  4. 级别4：添加示例
    • 包含2-3个带输入输出对的示例总结

Instruction Hierarchy

Instruction Hierarchy

[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]
[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]

Error Recovery

Error Recovery

Build prompts that gracefully handle failures:
  • Include fallback instructions
  • Request confidence scores
  • Ask for alternative interpretations when uncertain
  • Specify how to indicate missing information
构建能优雅处理故障的提示词：
  • 包含备选指令
  • 请求置信度评分
  • 在不确定时要求提供替代解释
  • 指定如何标识缺失的信息

Best Practices

最佳实践

  1. Be Specific: Vague prompts produce inconsistent results
  2. Show, Don't Tell: Examples are more effective than descriptions
  3. Test Extensively: Evaluate on diverse, representative inputs
  4. Iterate Rapidly: Small changes can have large impacts
  5. Monitor Performance: Track metrics in production
  6. Version Control: Treat prompts as code with proper versioning
  7. Document Intent: Explain why prompts are structured as they are
  1. 明确具体：模糊的提示词会产生不一致的结果
  2. 展示而非说明：示例比描述更有效
  3. 全面测试：在多样化、有代表性的输入上进行评估
  4. 快速迭代：微小的改动可能带来巨大影响
  5. 监控性能：在生产环境中跟踪指标
  6. 版本控制：将提示词视为代码进行适当的版本管理
  7. 记录意图：解释提示词的结构设计原因

Common Pitfalls

常见陷阱

  • Over-engineering: Starting with complex prompts before trying simple ones
  • Example pollution: Using examples that don't match the target task
  • Context overflow: Exceeding token limits with excessive examples
  • Ambiguous instructions: Leaving room for multiple interpretations
  • Ignoring edge cases: Not testing on unusual or boundary inputs
  • 过度设计：在尝试简单提示词之前就使用复杂的提示词
  • 示例污染：使用与目标任务不匹配的示例
  • 上下文溢出：过多示例导致超出token限制
  • 指令模糊：留下多种解释的空间
  • 忽略边缘情况：未在异常或边界输入上进行测试