The text discusses the importance of building an LLM evaluation framework to systematically identify the best hyperparameters for LLM systems. The author shares their personal experience of struggling with interruptions from new model releases and how they created DeepEval, an open-source LLM evaluation framework, to address this challenge. The framework is designed to evaluate and test LLM applications on various criteria, including contextual relevancy and summarization metrics. However, the author acknowledges that building such a framework can be challenging due to issues with synthetic data generation, accuracy, and robustness of LLM evaluation metrics, efficiency of the framework, and caching results. The text concludes by recommending DeepEval as a robust and working solution for LLM evaluation, offering 14+ research-backed metrics, integration with Pytest for CI/CD, and optimization features.