Effective language model evaluation requires both automated metrics & human judgment to capture quantitative performance and qualitative experience.

Automated metrics like BLEU, ROUGE, and BERTScore provide scalable benchmarking but miss nuanced aspects like coherence and factuality humans assess.

Human-in-the-loop evaluation frameworks need clear rubrics, pairwise comparisons, and feedback mechanisms to ensure reliable and actionable insights

Comprehensive evaluation strategies directly inform business decisions around model selection, fine-tuning priorities & deployment readiness.