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Original

English
🇨🇳

Translation

Chinese

AI-Native Startup Patterns

AI原生创业模式

When This Skill Activates

本技能的激活场景

Claude uses this skill when:
  • Building AI-first products
  • Implementing prompt engineering
  • Creating AI-native workflows
  • Scaling AI products efficiently
当Claude遇到以下场景时会使用本技能：
  • 构建AI优先产品
  • 实施Prompt Engineering
  • 创建AI原生工作流
  • 高效扩缩AI产品

Core Frameworks

核心框架

1. AI-Native Startup Playbook (Source: Dan Shipper - 5 products, 7-fig revenue, 100% AI)

1. AI原生创业手册（来源：Dan Shipper - 5款产品，7位数营收，100% AI驱动）

Key Principles:
  • Build fast with AI
  • Test with real users immediately
  • Iterate based on usage
  • Focus on distribution, not just product
核心原则：
  • 借助AI快速构建
  • 立即用真实用户测试
  • 根据使用反馈迭代
  • 聚焦获客，而非仅关注产品

2. 2025 Prompt Engineering Best Practices

2. 2025年Prompt Engineering最佳实践

Modern Approach:
- Use structured outputs (JSON)
- Implement streaming
- Design for retry logic
- Plan for model switching
- Cache aggressively
现代方法：
- 使用结构化输出（JSON）
- 实现流式传输
- 设计重试逻辑
- 规划模型切换
- 积极缓存

3. Cost Optimization

3. 成本优化

Strategies:
  1. Caching: 80% of queries can be cached
  2. Model routing: Simple → small model, complex → large model
  3. Batching: Group similar requests
  4. Prompt optimization: Minimize tokens
策略：
  1. 缓存： 80%的查询可被缓存
  2. 模型路由： 简单查询→小型模型，复杂查询→大型模型
  3. 批量处理： 归组相似请求
  4. Prompt优化： 最小化Token数量

Action Templates

行动模板

Template: AI Product Implementation

模板：AI产品实现

typescript
// Modern AI product pattern (2025)

interface AIFeature {
  // Streaming for responsiveness
  async *stream(prompt: string): AsyncGenerator<string> {
    const cached = await checkCache(prompt);
    if (cached) return cached;
    
    // Route to appropriate model
    const model = this.selectModel(prompt);
    
    for await (const chunk of model.stream(prompt)) {
      yield chunk;
    }
  }
  
  // Model selection (cost optimization)
  selectModel(prompt: string): Model {
    if (this.isSimple(prompt)) {
      return this.smallModel; // Fast, cheap
    } else {
      return this.largeModel; // Smart, expensive
    }
  }
  
  // Retry logic (reliability)
  async withRetry<T>(fn: () => Promise<T>): Promise<T> {
    for (let i = 0; i < 3; i++) {
      try {
        return await fn();
      } catch (e) {
        if (i === 2) throw e;
        await sleep(Math.pow(2, i) * 1000);
      }
    }
  }
}
typescript
// Modern AI product pattern (2025)

interface AIFeature {
  // Streaming for responsiveness
  async *stream(prompt: string): AsyncGenerator<string> {
    const cached = await checkCache(prompt);
    if (cached) return cached;
    
    // Route to appropriate model
    const model = this.selectModel(prompt);
    
    for await (const chunk of model.stream(prompt)) {
      yield chunk;
    }
  }
  
  // Model selection (cost optimization)
  selectModel(prompt: string): Model {
    if (this.isSimple(prompt)) {
      return this.smallModel; // Fast, cheap
    } else {
      return this.largeModel; // Smart, expensive
    }
  }
  
  // Retry logic (reliability)
  async withRetry<T>(fn: () => Promise<T>): Promise<T> {
    for (let i = 0; i < 3; i++) {
      try {
        return await fn();
      } catch (e) {
        if (i === 2) throw e;
        await sleep(Math.pow(2, i) * 1000);
      }
    }
  }
}

Template: AI Cost Budget

模板：AI成本预算

markdown
undefined
markdown
undefined

AI Cost Analysis: [Feature]

AI Cost Analysis: [Feature]

Current Usage

Current Usage

  • Daily requests: [X]
  • Model: [GPT-4/Claude/etc.]
  • Cost per 1K requests: [$X]
  • Monthly cost: [$Y]
  • Daily requests: [X]
  • Model: [GPT-4/Claude/etc.]
  • Cost per 1K requests: [$X]
  • Monthly cost: [$Y]

Optimization Plan

Optimization Plan

1. Caching (Est. 80% hit rate)

1. Caching (Est. 80% hit rate)

  • Before: [100]% paid calls
  • After: [20]% paid calls
  • Savings: [80]%
  • Before: [100]% paid calls
  • After: [20]% paid calls
  • Savings: [80]%

2. Model Routing

2. Model Routing

  • Simple queries ([60]%): Small model
  • Complex queries ([40]%): Large model
  • Savings: [50]%
  • Simple queries ([60]%): Small model
  • Complex queries ([40]%): Large model
  • Savings: [50]%

3. Batching

3. Batching

  • Real-time: [X]% of requests
  • Batchable: [Y]% of requests
  • Savings: [Z]%
  • Real-time: [X]% of requests
  • Batchable: [Y]% of requests
  • Savings: [Z]%

Projected Cost

Projected Cost

  • Before optimization: [$X/month]
  • After optimization: [$Y/month]
  • Reduction: [Z]%

---
  • Before optimization: [$X/month]
  • After optimization: [$Y/month]
  • Reduction: [Z]%

---

Quick Reference

快速参考

🤖 AI Startup Checklist

🤖 AI创业检查清单

Build:
  • Streaming implemented
  • Retry logic added
  • Model switching supported
  • Structured outputs (JSON)
Optimize:
  • Caching implemented
  • Model routing (simple vs complex)
  • Prompt tokens minimized
  • Batch processing where possible
Scale:
  • Cost per user < $X
  • Latency < X seconds
  • Error rate < X%
  • Model swappable (not locked in)
构建阶段：
  • 已实现流式传输
  • 已添加重试逻辑
  • 支持模型切换
  • 已配置结构化输出（JSON）
优化阶段：
  • 已实现缓存
  • 已配置模型路由（简单vs复杂）
  • 已最小化Prompt Token数量
  • 已在可行场景下采用批量处理
扩缩阶段：
  • 单用户成本 < $X
  • 延迟 < X秒
  • 错误率 < X%
  • 模型可替换（无锁定）

Real-World Examples

真实案例

Example: Dan Shipper's AI Products

案例：Dan Shipper的AI产品

Approach:
  • Built 5 AI products in 12 months
  • All using AI end-to-end
  • Revenue: 7 figures
  • Team: Small, AI-augmented
Key Insights:
  • Ship fast, learn from users
  • AI makes small teams powerful
  • Distribution > perfect product
方法：
  • 12个月内构建5款AI产品
  • 全部端到端采用AI驱动
  • 营收：7位数
  • 团队：小型AI增强团队
核心洞察：
  • 快速发布，从用户处学习
  • AI让小型团队拥有强大能力
  • 获客 > 完美产品

Key Quotes

关键引用

Dan Shipper:
"AI doesn't replace PMs. It makes small PM teams as powerful as large ones."
On Prompt Engineering:
"The best prompts in 2025 are structured, explicit, and tested with evals."
Brandon Chu:
"Build for the AI you'll have in 6 months, not the AI you have today."
Dan Shipper：
"AI不会取代产品经理，它让小型产品经理团队拥有大型团队的实力。"
关于Prompt Engineering：
"2025年最佳的Prompt是结构化、明确且经过评估测试的。"
Brandon Chu：
"为6个月后你将拥有的AI构建产品，而非你现在拥有的AI。"