The End of “Expected Result”: Why Traditional QA Fails in the AI Era Our Journey at Trendyol: When AI Met Quality Assurance With the rise of LLM-powered applications, Trendyol accelerated its AI …

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Traditional QA approaches fail when testing AI-powered systems because they rely on deterministic assertions and exact matching. Five key challenges emerge: the oracle problem (no objective correctness for creative outputs), non-determinism (same inputs produce different outputs), hallucinations (confident but incorrect responses), adversarial attacks (prompt injection and PII leaks), and business alignment (tone and user experience). Solutions include semantic similarity testing instead of exact matches, probabilistic assertions with consistency thresholds, RAG verification to cross-check AI claims against source data, adversarial test suites for security, and heuristic constraints for tone validation. Testing AI requires statistical thinking, security mindset, and treating consistency across multiple runs as success rather than identical outputs.