In traditional software development, you ship code and it either works or it doesn't—a button click triggers an action, a database query returns results, an API responds with expected data. AI products shatter this binary paradigm entirely, operating in a probabilistic universe where 'correct' exists on a spectrum and yesterday's perfect response might be tomorrow's embarrassing failure.
123456789101112from anthropic import Anthropic import json from dataclasses import dataclass from typing import List, Optional @dataclass class EvaluationResult: score: int # 1-5 scale reasoning: str dimension: str confidence: float
123456789101112import json from datetime import datetime from dataclasses import dataclass from typing import List, Dict, Optional import asyncio @dataclass class EvalCase: id: str input: str expected_output: str category: str
123456789101112import numpy as np from scipy import stats from dataclasses import dataclass from typing import List, Tuple, Optional @dataclass class ABTestResult: metric_name: str control_mean: float treatment_mean: float relative_lift: float p_value: float