Traditional software testing is insufficient for Large Language Models because LLMs are probabilistic rather than deterministic. Building trustworthy AI systems requires a comprehensive evaluation strategy that combines automated evaluations, human judgment, statistical analysis, safety testing, continuous monitoring, and AI governance throughout the software lifecycle.
Explores how Large Language Models (LLMs) are transforming software testing from deterministic validation to probabilistic quality evaluation. It covers modern testing approaches, including semantic evaluation, LLM-as-a-Judge, RAG testing, CI/CD integration, continuous monitoring, and AI governance to ensure reliable, secure, and trustworthy GenAI applications.