The text highlights the importance of evaluating and monitoring Large Language Models (LLMs) during both development and post-deployment phases. As LLMs become increasingly integrated into various applications, their performance can deviate from training-time performance due to model drift. The article emphasizes that 75% of businesses experience a decline in AI model performance over time without proper monitoring. Continuous monitoring with real-time alerts on various metrics is crucial to maintain model reliability and proactively address issues. Evaluating LLMs requires considering various metrics, including accuracy, precision, recall, F1 score, and similarity metrics like BLEU and ROUGE. Human evaluators are invaluable in providing insights into the nuanced performance of LLMs, especially for open-ended or complex tasks. Automated methods offer scalability and consistency, while tools like Galileo provide capabilities to identify and mitigate biases in real-time, enhancing the fairness and ethical integrity of AI systems. The article concludes that embracing the right metrics, frameworks, and techniques is essential to enhance AI systems' reliability and performance.