This whitepaper examines the limitations of traditional evaluation metrics when applied to modern GenAI systems. It explains how earlier metrics like BLEU, ROUGE, and exact match accuracy fail to capture the quality of generated responses, especially in systems using RAG architectures. The paper highlights challenges such as hallucinations, synthesis of information, and non-deterministic outputs. It emphasizes that GenAI systems generate context-aware, human-like responses, making traditional scoring methods inadequate. The document proposes the need for new evaluation approaches focused on trust, reliability, relevance, and real-world usefulness. It concludes that organizations must shift toward holistic, outcome-based evaluation frameworks to accurately measure GenAI system performance.

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