Testing Probabilistic Systems Deterministically 

Your AI systems are already live—powering decisions, generating insights, and accelerating innovation. But unlike traditional software, these systems behave probabilistically. Outputs differ from run to run, models evolve, data shifts, and user prompts introduce variability. 

Conventional QA—built around fixed requirements, deterministic outcomes, and binary pass/fail results—cannot provide assurance for systems where no single “correct” answer exists. 
So how do you validate, trust, and scale AI when certainty is no longer binary? 

This whitepaper draws on Zuci’s experience validating AI systems across industries and the research conducted at our AI Center of Excellence, led by the author. The frameworks presented—especially the proprietary Determinism Spectrum—are grounded in real enterprise deployments across banking, market research, healthcare, and technology.  

Inside This Whitepaper, You’ll find: 

• Why Traditional QE Fails for AI: Requirements aren’t fixed, test cases can’t have single expected outputs, and “defects” become ambiguous in probabilistic systems 

• Five Measurable Dimensions of AI Quality: Reproducibility, Factuality, Bias/Fairness, Drift/Stability, and Explainability, with practical testing methods for each 

• Zuci’s Determinism Spectrum Framework: Zone-by-zone testing strategies that match your QE approach to your AI use case, from predictive models to generative content 

• Proven Methodologies: Everest Group’s Quality@360° approach and real validation techniques including golden sets, rubric scoring, and human-in-the-loop evaluation