ai-startup-building

Claude uses this skill when:

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

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

---

Template: AI Product Implementation

```typescript

// Modern AI product pattern (2025)

interface AIFeature {

// Streaming for responsiveness

async *stream(prompt: string): AsyncGenerator {

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(fn: () => Promise): Promise {

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

```markdown

# AI Cost Analysis: [Feature]

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

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]%

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

```

---

🤖 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)

---

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

---

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."