Evaluating large language models (LLMs) involves a complex and iterative process that combines both offline and online methods to ensure comprehensive assessment of their performance. The challenges lie in defining clear evaluation goals, managing costs, and aligning automated tools with human perspectives. Effective evaluation incorporates a mixed-method approach, including user feedback, human annotation, and automated metrics, with traces playing a crucial role in capturing detailed logs of interactions for analysis. Application-specific challenges, such as those in retrieval-augmented generation and agent-based applications, require tailored metrics and evaluation strategies. Ultimately, maintaining a balanced evaluation strategy that adapts to evolving models and user needs is essential for developing reliable LLM applications.