How-To Guide
How to Evaluate LLM Quality
Measuring and monitoring AI output quality in production.
· 5 min read
Evaluating LLM quality is challenging because outputs are subjective and context-dependent. Here's a practical framework.
Evaluation Methods
- Automated: Hallucination detection, toxicity, PII scanning
- LLM-as-judge: Use another model to evaluate quality
- Human eval: Sample review for ground truth
- Task metrics: Accuracy, completion, user satisfaction
Key Metrics
- Hallucination rate: % of outputs with fabricated facts
- Policy violation rate: % of unsafe or inappropriate outputs
- PII detection rate: % of outputs leaking personal info
- Latency: Response time distribution
- User satisfaction: Thumbs up/down, NPS
Continuous Monitoring
- Track metrics over time for drift detection
- Compare against industry benchmarks
- Alert on quality degradation
- A/B test model and prompt changes
How do I evaluate quality?
Use multiple approaches: 1) Automated metrics like hallucination detection and toxicity scoring, 2) LLM-as-judge for subjective quality, 3) Human evaluation for ground truth, 4) Task-specific metrics like accuracy or completion rate.
What metrics should I track?
Key metrics: hallucination rate, policy violation rate, PII detection rate, latency, user satisfaction, task completion rate. Compare against industry benchmarks for your sector.
Automated quality evaluation
8 detection types with industry benchmarks.
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