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…