Module 725 minutes

Testing and Evaluation

Test AI systems systematically. Build evaluation frameworks and catch issues before users do.

testingevaluationevalsquality-assurance

✓Build AI evaluation frameworks
✓Create test datasets
✓Measure quality metrics
✓Implement continuous evaluation

Test AI Like You Test Code

AI systems need rigorous testing, just different approaches.

Evaluation Types

1. Unit tests: Individual prompts
2. Integration tests: Full workflows
3. Regression tests: Prevent degradation
4. Human evaluation: Sample review

Building Test Datasets

Real user inputs
Edge cases
Known correct outputs
Adversarial examples

Metrics to Track

Accuracy/correctness
Response time
Cost per request
User satisfaction
Error rate

Evals Framework

```python
def evaluate_response(output, expected):
return {
'correct': output == expected,
'similarity': semantic_similarity(output, expected),
'format_valid': validate_json(output)
}
```

Key Takeaways

→Build test datasets from real user inputs
→Automate evaluation where possible
→Always include human review samples
→Track metrics over time
→Test edge cases and adversarial inputs

Practice Exercises

Apply what you've learned with these practical exercises:

1.Create eval dataset for your use case
2.Implement automated evaluation
3.Set up monitoring dashboard
4.Run regression tests

Related Guides

→ What Are Ai Evals

Learning Objectives