Generative AI Testing: An Overview
Generative AI testing evaluates AI systems based on output quality, safety, bias, consistency, and reliability, diverging from traditional QA, which focuses solely on fixed outcomes. Unlike conventional quality assurance that verifies correctness, generative AI testing assesses crucial factors such as variability, context sensitivity, hallucinations, and ethical risks continuously. Key stakeholders like QA teams, product managers, AI engineers, and compliance teams utilize generative AI testing tools to validate AI behavior before and after deployment. While no-code AI testing tools are reliable for many use cases and help reduce maintenance, complex AI systems still require expert oversight and human judgment. To choose the right generative AI testing tool, teams should prioritize scalability, AI-specific evaluation metrics, ease of integration, data security, and the right balance between automation and human control, ensuring optimized performance and compliance.
