Software Quality Assurance Blogs

  AI-powered platforms are reshaping how we interact with software, but they also introduce new challenges for quality assurance (QA). Unlike rule-based or deterministic systems, AI features behave probabilistically i.e. they can produce varied, sometimes unexpected outputs even for the same input. This blog explores how to approach testing AI systems and  what makes them different. Traditional QA vs. AI QA While foundational QA practices like validating data flows, checking UI behavior, and reporting bugs still apply, AI systems add an extra layer of complexity. The testing approach depends on the type of AI system being used: Generative AI (such as chatbots, language models): These systems generate or create new content (text, voice, images) based on probabilistic models. They don’t follow fixed rules and may return varied outputs for the same input. QA focus : hallucination, tone, misinformation, safety, escalation, bias, prompt injection. Non-generative AI (such as recomm...