Understanding Evaluation Types
Learn the differences between unit tests, human evaluation, LLM judges, and A/B testing.
The EvalGate supports four distinct evaluation methodologies, each serving different purposes in your AI development lifecycle. Understanding when to use each type is key to building reliable AI systems.
1. Unit Tests
Best for: Regression testing, deterministic behavior, continuous integration
How It Works
Unit tests define explicit input/output pairs and assertion rules. They pass or fail based on deterministic criteria like exact string matching, regex patterns, or programmatic checks.
When to use:
- Testing specific functionality (e.g., "extract email from text")
- Preventing regressions after model updates
- CI/CD pipelines requiring fast, automated checks
- Validating structured outputs (JSON, SQL, code)
Limitations: Can be brittle for creative or open-ended tasks.
2. Human Evaluation
Best for: Subjective quality, nuanced tasks, ground truth establishment
How It Works
Human evaluators review AI outputs and provide ratings or feedback. The platform presents test cases to annotators, collects their judgments, and aggregates results into quality scores.
When to use:
- Evaluating creative content (writing, design, recommendations)
- Assessing helpfulness, tone, or empathy
- Creating gold-standard datasets for training LLM judges
- When automated metrics miss important nuances
Limitations: Slow, expensive, and doesn't scale to large test suites.
3. LLM Judge
Best for: Scalable quality assessment, complex reasoning tasks, mimicking human judgment
How It Works
A separate LLM (the "judge") evaluates outputs from your target LLM based on custom rubrics. The judge scores outputs on dimensions like accuracy, relevance, helpfulness, or safety.
When to use:
- Scaling human-like evaluation to 1000s of test cases
- Assessing open-ended tasks where exact matches aren't possible
- Multi-dimensional scoring (accuracy + tone + safety)
- Continuous monitoring of production outputs
Best practices:
- Train judges on human-labeled examples
- Use powerful models (GPT-4, Claude) as judges
- Create detailed rubrics with examples
- Validate judge alignment with human ratings
Limitations: Judges can have biases and may not catch all edge cases.
4. A/B Testing
Best for: Production experimentation, comparing model versions, data-driven decisions
How It Works
Traffic is split between two variants (e.g., old prompt vs. new prompt, GPT-3.5 vs. GPT-4). Statistical analysis determines which performs better based on real user interactions.
When to use:
- Testing prompt changes before full rollout
- Comparing model performance (GPT-4 vs. Claude)
- Optimizing for user engagement metrics
- Validating hypothesis about quality improvements
Metrics to track:
- User satisfaction (thumbs up/down, ratings)
- Task completion rates
- Latency and cost
- Conversion or retention metrics
Limitations: Requires significant traffic and time to reach statistical significance.
Combining Evaluation Types
The most robust evaluation strategies use multiple types together:
Example Workflow:
- Development: Run unit tests on every commit
- Pre-release: Use LLM judge on comprehensive test suite
- Validation: Human evaluation on sample of critical cases
- Production: A/B test with real users to confirm improvements
Choosing the Right Type
| Scenario | Recommended Type |
|---|---|
| Fast CI/CD checks | Unit Tests |
| Establishing ground truth | Human Evaluation |
| Scaling quality checks | LLM Judge |
| Production optimization | A/B Testing |