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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.
I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address the unique challenges introduced by Large Language Models (LLMs). What I expected
Breaking the Black Box: From Traditional QA to AI Assurance and Governance Read More »
Breaking the Black Box: From Traditional QA to AI Assurance and Governance I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address
The way we test software is changing — and it’s changing fast. Over the past couple of years, AI has moved from a buzzword in QA discussions to something teams are actively building into their workflows. If you’re working in software quality or engineering, understanding these shifts isn’t optional anymore. It’s part of staying effective.
Introduction Manual QA is slowing your releases more than your code is. Engineering teams across the US are hitting a ceiling where testing cycles cannot keep up with deployment speed. According to the 2025 World Quality Report, nearly 40% of delays in software delivery are tied directly to testing inefficiencies. Here’s the problem. You cannot
How AI Test Automation Fits Into ADLC and Why It Is Replacing Manual QA Read More »
Introduction While many testers today are exploring AI tools, I focused on understanding how applications behave under real-world load conditions.. As a QA engineer, my focus has mostly been on functional testing. But one important question always stayed in my mind: What happens when multiple users access the system at the same time? Will the
Exploring Modern Load Testing with k6 – A QA’s Practical Journey Read More »
If you’ve spent any time working in test automation, you know the drill: We start every project with the best intentions — promising ourselves we’ll write clean, maintainable code But fast forward a few months, and we’re spending half our week fixing flaky tests, hunting down brittle locators, and dealing with “duplicate email” errors in
Why I’ll Never Manually Write Test Scripts Again (And You Shouldn’t Either) Read More »
Artificial Intelligence is no longer a futuristic concept or an experimental capability. In 2026, AI has firmly embedded itself into core business operations—powering decisions in hiring, finance, healthcare, customer experience, and beyond. This shift brings a fundamental change: AI risk is now business risk. For Quality Engineering teams, especially QA leaders, this marks a turning
AI Risk & Compliance in 2026: Why QA Teams Must Lead the Shift Read More »
