Advanced AI Evaluation Frameworks
Build comprehensive evaluation systems: automated testing, human-in-the-loop, LLM-as-judge, and continuous monitoring.
TL;DR
Advanced evaluation combines automated metrics, LLM-based judging, human review, and production monitoring. Build comprehensive test suites covering accuracy, safety, and edge cases.
Evaluation dimensions
Accuracy: Correctness of outputs
Relevance: On-topic, addresses query
Coherence: Logical, well-structured
Safety: No harmful content
Groundedness: Based on provided context
Consistency: Similar inputs → similar outputs
LLM-as-judge
Use strong model (GPT-4) to evaluate weaker model outputs:
- Define rubric
- Provide reference answers (optional)
- LLM scores responses (1-5 or pass/fail)
Advantages: Scalable, nuanced
Limitations: Judge model biases, not perfect correlation with humans
Human evaluation
When needed:
- Initial benchmarking
- Periodic audits
- Edge cases
- Sensitive applications
Best practices:
- Clear rubrics
- Multiple raters
- Inter-rater agreement
- Representative samples
Automated test suites
Build datasets with expected outputs:
- Golden test sets
- Regression tests
- Adversarial examples
- Edge cases
Run on every model change, track metrics over time.
A/B testing in production
Compare models or prompts on real traffic:
- Random assignment
- Track business metrics
- Statistical significance
Continuous monitoring
- Sample production outputs
- Automated scoring
- Alert on degradation
- Human review of failures
Evaluation frameworks
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Key Terms Used in This Guide
Evaluation (Evals)
Systematically testing an AI system to measure how well it performs on specific tasks or criteria.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligence—like understanding language, recognizing patterns, or making decisions.
LLM (Large Language Model)
AI trained on massive amounts of text to understand and generate human-like language. Powers chatbots, writing tools, and more.
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