AI-Augmented QA 2026: Testing AI-Powered Applications

This course contains the use of artificial intelligence.

That is not a disclaimer — it is part of the point. The slides and the voice narrating the lectures were produced with AI tools, but every script was written and fact-checked by a human instructor with real hands-on experience. Think of the AI as the production crew; the teaching is human.

For decades, testing rested on one simple idea: give the system an input, know the correct output, and check whether they match.

AI-powered features break that completely. The same input can produce different outputs, there's often no single right answer, and correctness becomes a matter of judgment. The old playbook doesn't just need tweaking — parts of it stop working entirely.

What you will learn:

Testing AI-Powered Applications teaches you to test software that thinks probabilistically — and stay in control of quality.

You'll shift from checking exact outputs to evaluating behavior against clear criteria. You'll see how to design inputs that truly probe an AI, and how to hunt the failure modes that matter — hallucination, prompt injection and jailbreaks, bias, and unsafe output. You'll learn how to handle non-determinism and regression, understand the whole system around the model, and learn where AI can help you test AI, and where it can't.

What this course covers, module by module:

Why Testing AI Is Different; The AI Features You'll Test; Shifting Your Testing Mindset; Designing Test Inputs for AI; Evaluating AI Outputs; Correctness & Hallucination; Robustness & Adversarial Inputs; Testing for Bias & Fairness; Safety, Guardrails & Harmful Output; Non-Determinism & Regression; Testing the System Around the AI; Using AI to Test AI; Pitfalls & Responsible AI Testing.

Who this is for:

This is for testers, SDETs, and QA professionals being asked to test AI features — one of the most valuable and future-proof skills in all of quality engineering.

The whole course rests on one shift: you evaluate behavior, judge acceptability, and probe relentlessly for where it fails.

Go test something intelligent.

By the end, you will have a clear command of the topic — and the reference materials to keep using it long after the course.

Enroll now.

Please read — what this course is (and isn't): This is a theoretical, concept-first course. It gives you the mental models and the big picture — the what and the why — in plain language, so that even complete beginners can follow. The lessons are clear slides with narration, not hands-on labs: there are no coding exercises or step-by-step build-alongs, and each topic is a general overview rather than a deep technical dive. If you're looking for hands-on practice or an advanced, in-depth implementation course, this isn't the right fit — and that's by design. Start here to build the understanding; go hands-on elsewhere once the mental model clicks.