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AI Assurance sounds straightforward until an AI system is deployed in the real world. A chatbot may produce a convincing but incorrect answer. An AI agent may follow instructions hidden inside a document. A model upgrade may change previously reliable responses. An AI test-generation tool may create test cases that look correct but miss
AI Assurance in Action: Practical Testing Scenarios for AI Systems Read More »
Stable systems. Trustworthy AI. Safe automation. Reliable releases. Strong customer experiences. Software testing is entering a new phase. For years, QA teams have focused on functional testing, regression testing, automation, integration testing, and release validation. These practices remain important, but AI is changing how quality is achieved. Repetitive activities such as regression execution, test-data
I’ve been in QA for a few years now. I know how testing works. You write a test case. You define the expected result. You run it. It either passes or fails. Simple. So when our team started working on an AI-powered feature, I thought, okay, same process. Different kind of input, but same idea.
Quality Assurance has always been about understanding risk and validating systems before they reach production. After more than eight years in QA and now working in AI security, governance, and red teaming, I often compare traditional testing practices with the challenges introduced by AI systems. While the risks have evolved from software defects to hallucinations,
Lessons from IMDA’s LLM Testing Starter Kit: An AI Assurance Perspective Read More »
