Large Language Models (LLMs) are increasingly being used for natural language processing applications. However, monitoring their performance and ensuring ethical use is becoming more challenging due to the complexity of these models. This article discusses five best practices for monitoring LLMs: choosing the right metrics, setting up effective alerting systems, ensuring reliability and scalability with monitoring, running adversarial tests, and maintaining data integrity and model input. These practices help organizations ensure that their LLMs are reliable, safe, and effective in real-world scenarios.