Quick Answer (TL;DR)
Eval Pass Rate measures the percentage of AI outputs that pass a defined set of quality evaluation benchmarks --- including factual accuracy, format compliance, safety, and task completion. The formula is Outputs passing all eval criteria / Total outputs evaluated x 100. Industry benchmarks: Production systems: 80-95%, Safety-critical applications: >98%, Creative tasks: 70-85%. Track this metric as your primary AI quality gate before and after every model or prompt change.
What Is Eval Pass Rate?
Eval Pass Rate is the percentage of AI-generated outputs that meet your quality bar, as determined by a structured evaluation framework. Evaluations (evals) test outputs against specific criteria --- factual correctness, format adherence, safety compliance, relevance, completeness --- and an output passes only if it meets all required criteria.
This metric serves as the quality gate for AI systems in production. Unlike subjective user feedback, evals provide consistent, reproducible quality measurement. They are essential for regression testing (ensuring model updates do not degrade quality), A/B testing (comparing prompt strategies), and continuous monitoring (detecting quality drift in production).
Product managers should treat eval pass rate like a test suite pass rate in software development. Just as you would not ship code without passing tests, you should not ship prompt or model changes without passing evals. The eval suite itself requires ongoing maintenance --- as your AI features evolve, the eval criteria must evolve to match new requirements and edge cases.
The Formula
Outputs passing all eval criteria / Total outputs evaluated x 100
How to Calculate It
Suppose you run your evaluation suite against 500 AI outputs from the past week, evaluating each against 5 criteria (accuracy, format, safety, relevance, completeness). Of those 500 outputs, 430 pass all 5 criteria:
Eval Pass Rate = 430 / 500 x 100 = 86%
This tells you that 86% of outputs meet your full quality bar. For the 70 failures, break down which criteria failed most often. If 50 of 70 failures are format issues, that is a targeted fix. If failures are spread across all criteria, the model needs broader improvement.
Industry Benchmarks
| Context | Range |
|---|---|
| Production AI features (general) | 80-95% |
| Safety-critical applications | >98% |
| Creative and generative tasks | 70-85% |
| Code generation (functional correctness) | 60-80% |
How to Improve Eval Pass Rate
Build Thorough Eval Datasets
Your eval is only as good as its test cases. Build evaluation datasets that cover common cases, edge cases, adversarial inputs, and every failure mode you have seen in production. Aim for at least 200-500 eval cases per AI feature, refreshed quarterly.
Implement Multi-Criteria Evaluation
Evaluate outputs on multiple dimensions rather than a single pass/fail. Separate accuracy from formatting from safety from relevance. This granular approach identifies exactly which aspect of quality is failing and guides targeted improvements.
Use LLM-as-Judge with Human Calibration
Automated evaluation using a larger or specialized LLM scales well and provides consistent scoring. Calibrate your LLM judge against human evaluators on a sample set to ensure the automated scores align with human quality judgments. Recalibrate quarterly.
Create Regression Test Suites
Maintain a curated set of previously failing cases that were fixed. Run this regression suite before every model swap, prompt change, or system update to ensure fixes are not reverted. This prevents the common problem of fixing one issue while reintroducing another.
Monitor Eval Drift in Production
Run continuous sampling evaluations on production outputs, not just during development. Quality can drift as user behavior changes, data distributions shift, or API providers update their models. Set alerts when pass rate drops below your threshold.