Test-First AI Development

A framework for building reliable AI applications using test-driven development principles and breaking down complex tasks. Based on YC's Gary Tan's insights.

Core Philosophy for AI Development

• Write test cases before implementing AI functionality
• Avoid "raw dogging" prompts without proper testing
• Break down complex tasks into smaller, manageable steps
• Focus on real customer data and use cases
• Build worldview directly from customer experiences

Implementation Strategy

• Start with real business problems

  • Find companies spending significant money on knowledge work
  • Target businesses using offshore teams/call centers
  • Look for repetitive, rote knowledge work tasks

• Development Process

  • Get access to real customer data flows
  • Watch how current work is done
  • Write test cases based on actual usage
  • Implement with proper evals
  • Validate against real-world scenarios

Technical Guidelines

• Current LLM Capabilities

  • Models operate at ~120 IQ level work
  • Can handle structured, repetitive tasks well
  • Need careful prompt engineering

• Task Management

  • Break down complex prompts into smaller steps
  • Use chain of thought reasoning
  • Reduce context window load
  • Don't ask LLMs to do too much at once

Common Pitfalls to Avoid

• Creating demo-ware just to raise money
• Writing prompts without proper testing
• Giving LLMs too complex tasks
• Not using real customer data for validation
• Failing to implement proper evals

Future Considerations

• Expect continued improvements in model capabilities
• Plan for cost structures to decrease by 10x
• Focus on establishing brand and moats early
• Build systems that can scale with improving technology
• Maintain focus on real customer problems and data