After nineteen chapters of deep dives into specific aspects of AI product development, it's time to synthesize everything into a comprehensive, battle-tested playbook that takes you from initial spark to successful launch. This chapter provides the complete operational framework that companies like Linear, Vercel, and Notion use to consistently ship AI features in weeks rather than months.
123456789101112interface LaunchCriteria { name: string; check: () => Promise<boolean>; required: boolean; category: 'technical' | 'business' | 'legal' | 'operational'; } const launchCriteria: LaunchCriteria[] = [ { name: 'Error rate below threshold', check: async () => { const errorRate = await getErrorRate('last_24h');
123456789101112from dataclasses import dataclass from datetime import datetime, timedelta from typing import List, Dict import pandas as pd @dataclass class LaunchMetrics: feature_name: str launch_date: datetime def collect_metrics(self, days_post_launch: int = 14) -> Dict: end_date = self.launch_date + timedelta(days=days_post_launch)
123456789101112type RiskLevel = 'low' | 'medium' | 'high' | 'critical'; interface LaunchRiskAssessment { feature: string; factors: RiskFactor[]; overallRisk: RiskLevel; requiredApprovals: string[]; requiredChecklist: string; } interface RiskFactor { category: string;