This blog post discusses the importance of safeguarding and monitoring large language model (LLM) applications to prevent potential issues such as toxic prompts and responses or the presence of sensitive content. It explores three key aspects: content moderation, message auditing, and monitoring and observability. The implementation uses whylogs, LangKit, and WhyLabs tools to calculate and collect LLM-relevant text-based metrics for continuous monitoring. By incorporating these techniques, developers can ensure that prompts and responses adhere to predefined guidelines and avoid potential issues associated with LLMs.