Taming the Stochastic Beast: Building AI Evaluation Pipelines for GenAI Releases

If you've ever shipped a GenAI feature wondering “is this actually good enough?”, you're not alone. Traditional pass/fail QA breaks down when outputs are non-deterministic, and teams end up making release decisions based on subjective “vibe checks” rather than data. This session shows how Product Managers can partner with QA to replace intuition with a systematic AI evaluation pipeline. You'll learn how to define quality as measurable dimensions (groundedness, tone, helpfulness, safety), build a representative test set, and design rubrics that align product goals with engineering constraints. Nixalkumar will walk through a practical LLM-as-a-judge pattern used in production, including calibration against human ratings, inter-rater consistency checks, and a lightweight error taxonomy to scale grading across thousands of interactions. You'll also see how to turn scores into release gates: setting thresholds, catching regressions, monitoring drift, and maintaining an audit trail for leadership and compliance. You'll leave with templates you can adapt immediately: a rubric framework, test set plan, scorecard format, and a cross-functional operating model that helps teams ship GenAI features with confidence instead of crossed fingers.