awesome-copilot/plugins/phoenix/skills/phoenix-evals/references/validation.md

Validation

Validate LLM judges against human labels before deploying. Target >80% agreement.

Requirements

Requirement	Target
Test set size	100+ examples
Balance	~50/50 pass/fail
Accuracy	>80%
TPR/TNR	Both >70%

Metrics

Metric	Formula	Use When
Accuracy	(TP+TN) / Total	General
TPR (Recall)	TP / (TP+FN)	Quality assurance
TNR (Specificity)	TN / (TN+FP)	Safety-critical
Cohen's Kappa	Agreement beyond chance	Comparing evaluators

Quick Validation

from sklearn.metrics import classification_report, confusion_matrix, cohen_kappa_score

print(classification_report(human_labels, evaluator_predictions))
print(f"Kappa: {cohen_kappa_score(human_labels, evaluator_predictions):.3f}")

# Get TPR/TNR
cm = confusion_matrix(human_labels, evaluator_predictions)
tn, fp, fn, tp = cm.ravel()
tpr = tp / (tp + fn)
tnr = tn / (tn + fp)

Golden Dataset Structure

golden_example = {
    "input": "What is the capital of France?",
    "output": "Paris is the capital.",
    "ground_truth_label": "correct",
}

Building Golden Datasets

1. Sample production traces (errors, negative feedback, edge cases)
2. Balance ~50/50 pass/fail
3. Expert labels each example
4. Version datasets (never modify existing)

# GOOD - create new version
golden_v2 = golden_v1 + [new_examples]

# BAD - never modify existing
golden_v1.append(new_example)

Warning Signs

• All pass or all fail → too lenient/strict
• Random results → criteria unclear
• TPR/TNR < 70% → needs improvement

Re-Validate When

• Prompt template changes
• Judge model changes
• Criteria changes
• Monthly