How to Test an AI Feature When There Is More Than One Correct Answer
dev.to·
Traditional automated testing relies on deterministic assertions where a given input produces a single, exact expected output. AI-driven features break this paradigm because the same prompt can generate multiple distinct, equally valid responses. When QA workflows apply rigid string-matching tests to LLM outputs, legitimate answers get flagged as bugs while subtle factual errors might go completely undetected. The solution is shifting toward property-based evaluation rubrics. Instead of checking for rigid exact matches, test suites should define structural and domain criteria that any acceptable response must satisfy—such as verifying accurate policy citations, exact numerical pricing, or domain-specific constraints. Implementing property-based validation ensures that your test suites can accommodate variable phrasing while reliably catching confident-sounding hallucinations before they reach production users.