AI Follows the Prompt. Community Expands Quality Thinking
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Agenda
7 - 7:10 pm (UTC+2): Intro
7:10 - 8:00 pm (UTC+2): AI Follows the Prompt. Community Expands Quality Thinking (Ujjwal Kumar Singh)
8:00 - 8:15 pm (UTC+2): Q&A - Open Discussion
AI Follows the Prompt. Community Expands Quality Thinking
Artificial Intelligence can generate test cases, automation scripts, and even testing strategies within seconds. As these tools become more capable, it is tempting to believe that learning has never been easier. But AI has an important limitation: it works within the boundaries of the questions we ask.
Early in my testing career, I measured quality through test case counts and coverage metrics. I believed that more test cases meant better testing. If AI had been available then, it would have helped me generate more tests, faster. What it would not have done is challenge whether I was solving the right problem in the first place.
That shift came through community.
Through discussions, articles, meetups, and conversations with experienced practitioners, I was exposed to questions I had never considered. Why are we measuring coverage this way? What risk does this test actually reduce? What happens when a dependency fails? How do we know a system is resilient rather than merely tested?
In many ways, I do not think this experience is unique. As an industry, we are investing heavily in tools that accelerate execution, but much less in environments that challenge how we think about quality and risk. AI can help us generate hundreds of test cases for a login feature, but it will not automatically ask whether the authentication service can fail, whether cached tokens continue to work, or whether the system can degrade gracefully. Those questions usually emerge through exposure to different perspectives, real world failures, and conversations with other practitioners.
In this session, I will share my journey from test case execution toward risk based quality thinking and explore why AI often amplifies our existing assumptions while communities help expose the blind spots we do not know we have. We will look at practical examples that contrast feature focused testing with system level quality thinking and discuss how community participation, public sharing, and constructive disagreement can accelerate professional growth in ways that private upskilling often cannot.
This is not an anti AI talk. It is a discussion about how AI and communities complement each other. AI accelerates execution. Communities expand the questions worth asking.
Attendees will leave with practical examples, a framework for evaluating the limits of AI assisted testing, and a deeper understanding of how community driven learning can strengthen judgment, improve risk awareness, and support the transition from test execution toward quality engineering thinking.
Join Zoom Meeting:
https://us06web.zoom.us/j/84690820880
Meeting ID: 846 9082 0880
Passcode: 539512
See you there! 🙂
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