AI-Native Startup Patterns

When This Skill Activates

Claude uses this skill when:

• Building AI-first products
• Implementing prompt engineering
• Creating AI-native workflows
• Scaling AI products efficiently

Core Frameworks

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

Key Principles:

• Build fast with AI
• Test with real users immediately
• Iterate based on usage
• Focus on distribution, not just product

2. 2025 Prompt Engineering Best Practices

Modern Approach:

- Use structured outputs (JSON)
- Implement streaming
- Design for retry logic
- Plan for model switching
- Cache aggressively

3. Cost Optimization

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

Action Templates

Template: AI Product Implementation

// 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

# AI Cost Analysis: [Feature]

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

## Optimization Plan

### 1. Caching (Est. 80% hit rate)
- Before: [100]% paid calls
- After: [20]% paid calls
- Savings: [80]%

### 2. Model Routing
- Simple queries ([60]%): Small model
- Complex queries ([40]%): Large model
- Savings: [50]%

### 3. Batching
- Real-time: [X]% of requests
- Batchable: [Y]% of requests
- Savings: [Z]%

## Projected Cost
- Before optimization: [$X/month]
- After optimization: [$Y/month]
- Reduction: [Z]%

Quick Reference

🤖 AI Startup Checklist

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)

Real-World Examples

Example: Dan Shipper's AI Products

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

Key Quotes

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."