Leonard Tang

This talk introduces hazing, a form of fuzz testing adapted for Generative AI systems. It addresses the critical challenge of validating and verifying AI outputs, which are inherently subjective and unstructured. Traditional evaluation methods relying on static datasets are insufficient due to the brittle and non-deterministic nature of GenAI, where minor input variations can lead to drastically different outputs. Hazing aims to pressure-test AI systems through large-scale simulation and optimization before deployment to ensure robustness and reliability.