When evaluating Large Language Models (LLMs), a blend of quantitative and qualitative approaches is necessary to gain a comprehensive understanding of model performance. Quantitative evaluation focuses on numerical metrics, such as accuracy metrics, to objectively measure and compare model performance across various tasks, producing consistent, reproducible results that can be easily tracked over time to measure progress in model development. However, these methods lack depth and fail to capture nuanced performance across diverse contexts and use cases. Qualitative evaluations, on the other hand, examine aspects like coherence, relevance, and appropriateness through descriptive analysis or human judgment, providing actionable insights for model improvement but often being resource-intensive. By integrating both approaches, developers can overcome limitations, provide detailed insights into model behavior, and identify complex patterns or errors that humans might overlook, ultimately driving meaningful improvements in their AI systems.