In the AI product landscape, the companies that ship improvements daily consistently outperform those stuck in weekly or monthly release cycles. This isn't about moving fast and breaking things—it's about building systematic processes that let you learn from real users faster than your competitors.
123456789101112interface PromptVersion { id: string; promptId: string; version: number; content: string; systemInstruction: string; variables: Record<string, VariableDefinition>; metadata: { author: string; createdAt: Date; description: string; parentVersion: number | null;
123456789101112interface PromptVersion { id: string; version: string; // semver template: string; variables: string[]; modelId: string; evaluationScore: number; createdAt: Date; createdBy: string; rolloutPercentage: number; isActive: boolean; }
123456789101112// ab-testing-service.ts import { StatsigClient } from 'statsig-node'; import { PromptRegistry } from './prompt-registry'; import { MetricsCollector } from './metrics'; interface ExperimentConfig { experimentId: string; variants: { name: string; promptVersion: string; allocation: number; }[];
123456789101112# interview_scheduler.py from datetime import datetime, timedelta import random from typing import List, Dict from dataclasses import dataclass @dataclass class UserSegment: name: str criteria: Dict interview_quota: int priority: int
123456789101112# experiment_report.py from dataclasses import dataclass from typing import List, Optional import scipy.stats as stats import numpy as np @dataclass class ExperimentReport: experiment_id: str start_date: str end_date: str control_size: int