QE for AI: Testing Probabilistic Systems Deterministically

Traditional QE methods fail when applied to AI systems, blocking production-scale adoption. Requirements aren’t fixed; test cases can’t have single expected outputs, and “defects” become ambiguous in probabilistic systems. This leaves enterprises unable to provide the assurance leadership needs for mission-critical AI deployment. 

Watch this webinar recording in which experts from Zuci Systems and Everest Group share practical frameworks to measure AI quality.  

What You’ll Learn: 

• Why your current QE approach can’t handle AI (and what actually works instead) 

• Five measurable dimensions of AI quality 

• Zuci’s Determinism Spectrum framework for testing different AI systems 

• Practical AI validation methods: golden sets, rubric scoring, consensus evaluation, and human-in-the-loop testing 

Presented by Zuci Systems and Everest Group