From the course: AI Coding: Impacts on System Design and Architecture

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Define testing strategies for AI systems

Define testing strategies for AI systems

From the course: AI Coding: Impacts on System Design and Architecture

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Define testing strategies for AI systems

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- [Narrator] What if your AI system passed all your tests, but still gave users misleading, biased, or flat out wrong answers? That's the challenge we face in testing AI. Traditional tests aren't enough anymore, because AI systems don't just run. They reason, adapt, and sometimes improvise. And that means bugs don't always show up as broken code. Sometimes they show up as unpredictable behavior. Because of that, we need to rethink testing, not as a checklist of pass/fail conditions, but as a strategy for trust. Let's start with the basics. You'll still need your standard unit tests, integration tests, and performance benchmarks. These are foundational, especially if you're wrapping AI components inside traditional service layers. But that's just the beginning. When AI is involved, you have to test for behavior, not just function. That includes evaluating output quality. You might do this through human-in-the-loop review, confidence scoring, or even snapshot tests that check whether…

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