Evaluation & Benchmarking
Rigorous measurement is the difference between a prototype and a production system
Session Topics
Defining the Right Metrics
BLEU, ROUGE, BERTScore, and human evaluation — when each applies and why 'accuracy' alone is meaningless for generative AI.
Building Benchmark Suites
Creating evaluation datasets that reflect real-world performance — avoiding benchmark contamination and leaderboard gaming.
A/B Testing AI Systems
Pairwise comparison, statistical significance, confidence intervals — how to know if your new prompt is actually better.
Performance Monitoring
Drift detection, latency tracking, error rate monitoring, and cost analysis in production AI systems.
Failure Mode Analysis
Systematically finding where your system breaks: adversarial inputs, edge cases, distribution shift, and cascade failures.
Evaluate Your System Rigorously
Evaluate your hiring AI with real rigour: create a benchmark, establish a baseline, calculate metrics, run a statistical significance test, and produce a results report. This is how real AI teams validate their systems before deployment.
A benchmark evaluation report with metrics, statistical significance testing, and baseline comparison for your hiring AI.
Key Takeaways
- What gets measured gets improved — define metrics before building
- Human evaluation remains the gold standard for generation quality
- Statistical significance is mandatory; p-values matter in AI evaluation
- Monitoring is not optional — production AI systems degrade over time