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 design inputs that truly probe an AI, and hunt the failure modes that matter — hallucination, prompt injection and jailbreaks, bias, and unsafe output. You’ll handle non-determinism and regression, test 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.
