Every successful AI product you've used—ChatGPT, GitHub Copilot, Notion AI—was built by teams that internalized a specific set of principles that enabled them to ship at extraordinary speed. These aren't just nice-to-have guidelines; they're the fundamental operating system that determines whether your AI product launches in weeks or languishes in development for months.
123456789101112import anthropic client = anthropic.Anthropic() # Step 1: Define your test cases (real user examples) test_cases = [ {"input": "Meeting notes from product sync...", "expected": "Clear action items"}, {"input": "Long customer email complaint...", "expected": "Concise summary"}, # Add 8-18 more real examples ] # Step 2: Your candidate prompt
123456789101112# This is ALL the code you need for v1 # Don't build more until this is validated with users import anthropic from typing import Optional client = anthropic.Anthropic() def generate_response( user_input: str, context: Optional[str] = None ) -> str:
123456789101112interface PromptVariant { id: string; template: string; parameters: Record<string, string>; } interface TestResult { variantId: string; input: string; output: string; latencyMs: number; tokenCount: number;
123456789101112import hashlib import random from dataclasses import dataclass from typing import Optional import logging @dataclass class PromptExperiment: name: str control_prompt: str variant_prompt: str traffic_percentage: float = 0.1 # Start with 10%
123456789101112interface FeatureFlag { name: string; enabled: boolean; rolloutPercentage: number; userSegments?: string[]; killSwitch: boolean; } class AIFeatureFlags { private flags: Map<string, FeatureFlag> = new Map(); constructor(private configSource: ConfigSource) {