Back to Blog
How the Big Labs Actually Do Evals
NASA doesn't launch rockets without checklists. Every system gets verified, every anomaly investigated. The cost of a failed launch is too high for "it worked in testing." So they test everything, every time, with documented procedures.
The major AI labs treat model releases similarly. Evals aren't a final check before shipping. They're integrated into every step of development. A prompt change triggers automated evals. A failed eval blocks the change. No exceptions.
The CI/CD Eval Pipeline
def lab_style_eval_pipeline():
"""
How production eval systems actually work
"""
return {
"trigger_points": {
"every_commit": [
"Fast smoke tests (< 5 min)",
"Core capability checks",
"Safety classifier basics",
],
"every_pr": [
"Full eval suite (< 30 min)",
"Regression tests against baseline",
"Human review sample flagging",
],
"pre_release": [
"Extended eval suite (hours)",
"Red team testing",
"Human review of flagged cases",
"Sign-off required",
],
"post_release": [
"Continuous monitoring",
"Drift detection",
"User feedback integration",
],
},
"blocking_behavior": {
"fail_fast": "Any critical eval failure blocks merge",
"soft_failures": "Warnings logged, reviewed in aggregate",
"manual_override": "Requires senior engineer + documentation",
},
}Eval Categories at Scale
def eval_categories():
return {
"capability": {
"purpose": "Model can do what it should",
"examples": [
"MMLU for knowledge",
"HumanEval for code",
"MATH for reasoning",
"Task-specific benchmarks",
],
"frequency": "Every significant change",
"threshold": "No regression > 2% from baseline",
},
"safety": {
"purpose": "Model doesn't do what it shouldn't",
"examples": [
"Refusal on harmful requests",
"No PII generation",
"No illegal advice",
"Appropriate content filtering",
],
"frequency": "Every change",
"threshold": "Zero tolerance on critical categories",
},
"reliability": {
"purpose": "Model behaves consistently",
"examples": [
"Same question, consistent answer",
"Format compliance",
"No hallucination on verifiable facts",
],
"frequency": "Every change",
"threshold": "Consistency > 95%",
},
"alignment": {
"purpose": "Model follows instructions and intent",
"examples": [
"Follows system prompt",
"Respects constraints",
"Helpful without being harmful",
],
"frequency": "Every change",
"threshold": "Measured against human preference",
},
}Implementing Blocking Evals
class BlockingEvalSystem:
"""
Evals that gate deployments
"""
def __init__(self):
self.critical_evals = [
SafetyEval(),
CoreCapabilityEval(),
RegressionEval(),
]
self.warning_evals = [
StyleConsistencyEval(),
VerbosityEval(),
]
def run_suite(self, model_version: str, changes: dict) -> dict:
results = {
"critical": [],
"warnings": [],
"passed": True,
}
# Critical evals - any failure blocks
for eval in self.critical_evals:
result = eval.run(model_version)
results["critical"].append(result)
if not result["passed"]:
results["passed"] = False
results["blocking_reason"] = result["reason"]
# Warning evals - logged but don't block
for eval in self.warning_evals:
result = eval.run(model_version)
results["warnings"].append(result)
return results
def gate_decision(self, results: dict) -> dict:
if not results["passed"]:
return {
"decision": "BLOCKED",
"reason": results["blocking_reason"],
"action_required": "Fix failing evals before merge",
}
if len(results["warnings"]) > 0:
return {
"decision": "APPROVED_WITH_WARNINGS",
"warnings": results["warnings"],
"action_required": "Review warnings before release",
}
return {"decision": "APPROVED"}Regression Testing
class RegressionTestingSuite:
"""
Catch quality drops compared to baseline
"""
def __init__(self, baseline_version: str):
self.baseline = baseline_version
self.test_set = self.load_regression_set() # Curated examples
def detect_regression(self, new_version: str) -> dict:
baseline_results = self.evaluate(self.baseline, self.test_set)
new_results = self.evaluate(new_version, self.test_set)
regressions = []
improvements = []
for test_id in self.test_set:
baseline_score = baseline_results[test_id]
new_score = new_results[test_id]
if new_score < baseline_score - self.threshold:
regressions.append({
"test_id": test_id,
"baseline": baseline_score,
"new": new_score,
"delta": new_score - baseline_score,
})
elif new_score > baseline_score + self.threshold:
improvements.append({
"test_id": test_id,
"baseline": baseline_score,
"new": new_score,
"delta": new_score - baseline_score,
})
return {
"passed": len(regressions) == 0,
"regressions": regressions,
"improvements": improvements,
"summary": f"{len(regressions)} regressions, {len(improvements)} improvements",
}Human-in-the-Loop Evals
def human_eval_integration():
return {
"when_to_use_humans": [
"Subjective quality assessment",
"Edge cases flagged by automated evals",
"Safety-critical categories",
"New capability validation",
"Calibrating automated evals",
],
"scaling_human_review": {
"prioritization": "Focus humans on highest-impact reviews",
"sampling": "Statistical sampling for volume tasks",
"disagreement_resolution": "Multiple reviewers on edge cases",
"feedback_loop": "Human labels improve automated evals",
},
"implementation": """
class HumanEvalQueue:
def queue_for_review(self, response: dict, priority: str):
'''Add response to human review queue'''
if priority == "critical":
# Block deployment until reviewed
self.blocking_queue.add(response)
else:
# Review in background
self.background_queue.add(response)
def process_review(self, review: dict):
'''Handle completed human review'''
# Update eval calibration
self.calibration_data.add(review)
# If disagreement with automated eval
if review["human_score"] != review["automated_score"]:
self.flag_for_eval_improvement(review)
""",
}Monitoring Production Quality
class ProductionQualityMonitor:
"""
Continuous monitoring after deployment
"""
def __init__(self):
self.baseline_metrics = self.load_baseline()
self.alert_thresholds = {
"quality_score_drop": 0.05, # 5% drop
"error_rate_increase": 0.02, # 2% increase
"latency_increase_p99": 1.5, # 50% increase
}
def monitor_window(self, window_hours: int = 1) -> dict:
current_metrics = self.collect_metrics(window_hours)
alerts = []
# Check quality
if current_metrics["quality_score"] < self.baseline_metrics["quality_score"] - self.alert_thresholds["quality_score_drop"]:
alerts.append({
"type": "quality_regression",
"current": current_metrics["quality_score"],
"baseline": self.baseline_metrics["quality_score"],
"severity": "high",
})
# Check errors
if current_metrics["error_rate"] > self.baseline_metrics["error_rate"] + self.alert_thresholds["error_rate_increase"]:
alerts.append({
"type": "error_spike",
"current": current_metrics["error_rate"],
"baseline": self.baseline_metrics["error_rate"],
"severity": "critical",
})
return {
"status": "healthy" if len(alerts) == 0 else "degraded",
"alerts": alerts,
"metrics": current_metrics,
}The Eval Ownership Model
def eval_ownership():
return {
"model": {
"owner": "Team that built the feature",
"responsibility": [
"Write evals for new features",
"Maintain evals as requirements change",
"Fix regressions they introduce",
],
},
"eval_platform_team": {
"responsibility": [
"Eval infrastructure",
"CI/CD integration",
"Compute for running evals",
"Metrics and dashboards",
],
},
"safety_team": {
"responsibility": [
"Safety-critical evals",
"Red team testing",
"Veto power on releases",
],
},
"release_process": """
1. Developer writes feature + evals
2. CI runs automated suite
3. PR blocked if critical evals fail
4. Safety team reviews flagged cases
5. Release requires sign-off from:
- Feature owner
- Safety team (for safety-relevant changes)
- Eval platform team (for infra changes)
""",
}Evals at scale aren't about catching every possible issue. They're about creating a systematic process that catches the issues that matter most, integrating that process into the development workflow, and building the discipline to not ship when evals fail. The culture matters as much as the code.