Automating QA for AI Features: Red-Team, Eval Sets, and Guards
As artificial intelligence systems become increasingly integrated into products and services, the complexity and risk of errors, misuse, and unintended outputs rise significantly. For product owners, ML engineers, and QA professionals, developing a robust quality assurance (QA) architecture for AI features is no longer optional—it’s essential. With traditional testing techniques falling short of the dynamic and probabilistic nature of AI models, new methodologies like red-teaming, curated evaluation sets, and guard mechanisms are emerging as critical tools in the AI development lifecycle. Read more