Welcome to the Wild West of QA
As a Senior QA Engineer, I thought I’d seen it all—apps crashing, APIs throwing tantrums, and web platforms that break the moment you look at them funny. But then came Generative AI, a technology that doesn’t just process inputs; it creates. It writes, it chats, it even tries to be funny (but let’s be real, AI humor is still a work in progress).
And testing it? That’s like trying to potty-train a dragon. It’s unpredictable, occasionally brilliant, sometimes horrifying, and if you’re not careful, it might just burn everything down.
So, how do we QA something that makes up its own rules? Buckle up, because this is not your typical test plan.
1. Functional Testing: Is This Thing Even Working?
Unlike traditional software, where a button click does the same thing every time, Generative AI enjoys a little creative freedom. You ask it for a recipe, and it gives you a five-paragraph existential crisis. You request a joke, and it tells you one so bad you reconsider your life choices.
What to Test:
✅ Does it stay on topic? – Or does your AI assistant turn every conversation into a conspiracy theory?
✅ Can it handle weird inputs? – Because someone will ask it for a Shakespearean rap battle between a cat and a toaster.
✅ Does it contradict itself? – If it tells you coffee is good for you in one response and bad in the next, we’ve got a problem.
The goal isn’t to eliminate creativity—it’s to make sure the AI isn’t randomly creative when it shouldn’t be.
2. Bias and Ethical Testing: Keeping AI From Becoming a Jerk
AI learns from data, and let’s be honest—the internet is not always a great teacher. Left unchecked, AI can develop some questionable opinions faster than your uncle on Facebook.
How to Keep AI from Going Rogue:
🔹 Test diverse prompts – AI should treat everyone fairly, not just the data it was trained on.
🔹 Red teaming – Give it ethically tricky questions and see if it stays out of trouble.
🔹 Set boundaries – No AI should be giving out legal advice or telling people how to build a rocket in their backyard.
If your AI starts sounding like a 1950s sci-fi villain, shut it down immediately.
3. Prompt Testing: Because Users Will Absolutely Try to Break It
You think people will use AI responsibly? That’s adorable. Someone will try to make it swear, leak secrets, or write them a 10,000-word novel about sentient bananas.
How We Stay Ahead of the Chaos:
🛑 Adversarial Inputs – What happens when we feed it nonsense? (Asking for a friend.)
🛑 Jailbreak Attempts – Can users trick it into saying things it shouldn’t?
🛑 Security Testing – AI should not be taking financial advice from Reddit.
If a 12-year-old on the internet can trick your AI into revealing confidential data, you have failed.
4. Automation vs. Human Testing: The Perfect Odd Couple
Sure, we have automated tools that can scan for toxicity, bias, and nonsense—but AI is sneaky. It might pass an automated test while still giving users responses that sound like they were written by a sleep-deprived raccoon.
⚙️ Automated Tools: Find patterns, flag issues at scale.
👀 Human Reviewers: Check for the weird stuff automation misses.
Example: AI might avoid offensive words, but still generate an insult so polite it destroys your self-esteem. That’s where human testers step in.
5. Regression Testing: Making Sure AI Doesn’t Get Dumber
AI updates are like software updates—sometimes they fix things, and sometimes they introduce exciting new problems. A chatbot that used to answer correctly might suddenly think that 2 + 2 = potato.
How We Prevent “AI Brain Fog”:
🔄 Re-run old test cases – Make sure previous fixes stay fixed.
📊 Monitor response quality – No one wants their AI assistant to suddenly forget basic facts.
🚨 Check for unintended side effects – Did fixing bias make the AI too cautious? (Nobody wants an AI that refuses to answer anything.)
AI should evolve, not devolve.
6. Explainability: AI Should Not Sound Like a Fortune Cookie
Users need to trust AI, and that means it needs to justify its answers. If AI is just guessing but acting confident, that’s a huge problem.
Key Questions for Explainability Testing:
🔍 Does it cite sources? – Or is it just making things up?
🔍 Can it explain itself? – If you ask “why?” and it panics, that’s a bad sign.
🔍 Does it admit uncertainty? – “I don’t know” is a valid answer. “Of course, the sky is green” is not.
Trustworthy AI is transparent AI.
Final Thoughts: QA’s Role in AI’s Future
Testing Generative AI isn’t just about finding bugs—it’s about keeping AI from becoming a liability. We’re no longer just debugging code; we’re debugging intelligence itself.
It’s weird. It’s unpredictable. And it keeps me up at night.
But if I wanted a boring job, I’d be testing calculators. Instead, I get to shape the future of AI—one ridiculous test case at a time.
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