As AI products mature from prototypes to production systems serving millions of users, manual evaluation becomes impossible—you simply cannot have humans review every model response when you're processing 10 million requests per day. This chapter transforms you from someone who evaluates AI manually into a leader who builds automated evaluation infrastructure that scales with your product.
123456789101112from dataclasses import dataclass from typing import List, Callable, Dict, Any import asyncio from datetime import datetime @dataclass class TestCase: id: str input: str expected_traits: Dict[str, Any] category: str priority: int # 1=critical, 2=high, 3=medium
123456789101112from dataclasses import dataclass from typing import List, Dict, Callable from enum import Enum import asyncio class EvalPriority(Enum): BLOCKING = "blocking" # Must pass before deploy MONITORING = "monitoring" # Track but don't block EXPERIMENTAL = "experimental" # New evals being validated @dataclass class EvalConfig:
123456789101112from dataclasses import dataclass from typing import List, Dict, Callable import asyncio from datetime import datetime @dataclass class EvalConfig: name: str eval_set_path: str metrics: List[str] thresholds: Dict[str, float] sample_size: int = None # None = full eval set
123456789101112import numpy as np from scipy import stats from typing import List, Optional, Tuple from dataclasses import dataclass from enum import Enum class RegressionSeverity(Enum): NONE = 'none' WARNING = 'warning' # Potential regression, monitor closely SIGNIFICANT = 'significant' # Likely regression, investigate CRITICAL = 'critical' # Definite regression, block deployment
123456789101112from typing import List, Dict, Tuple import hashlib import random from dataclasses import dataclass @dataclass class EvalCase: id: str input: str expected: str priority: int # 1=critical, 2=important, 3=nice-to-have last_failure: Optional[datetime] = None