Testing AI applications is fundamentally different from testing traditional software. There's no deterministic output, prompts change behavior, and edge cases multiply. Here's how to build a robust testing strategy for AI-powered applications. The AI Testing Challenge Traditional testing: Input → Function → Expected Output AI testing: Input → Prompt + Context → Probabilistic Output You can't assert exact outputs. Instead, you test properties. Property-Based Testing for AI `typescript // Instead of testing exact output, test properties interface TestCase { input: string; constraints: Constraint[]; } interface Constraint { type: 'contains' | 'excludes' | 'length' | 'format' | 'json'; value: string | number | RegExp; } async function testAIOutput(testCase: TestCase, actualOutput: string): Promise { for (const constraint of testCase.constraints) { switch (constraint.type) { case 'contains': if (!actualOutput.includes(constraint.value as string)) return false; break; case 'excludes': if…