Traditional monitoring tools often fail to detect issues in LLM-powered applications, such as hallucinations or silent truncations. Developers must implement specialized production observability strategies to track quality drift, prompt failures, and latency. Effective systems rely on structured logging and automated evaluation rubrics to capture model performance issues that standard infrastructure metrics overlook.