The text discusses the challenges of testing Large Language Model (LLM) applications and introduces the concept of LLM evaluators, which are used to quantify how well an LLM system is performing on specific criteria. The article highlights that traditional software testing principles do not directly apply to LLMs due to their unpredictable nature, making it difficult to transfer traditional metrics. It then explains various types of LLM evaluators, including single-output evaluation and pairwise comparison, and discusses common metrics such as correctness, answer relevancy, faithfulness, task completion, and summarization. The article also mentions several frameworks that use LLM evaluators, including G-Eval, DAG, QAG, and Prometheus. It provides guidance on choosing the right LLM evaluator for a specific use case and system architecture, and discusses methods to optimize evaluation quality, such as using CoT prompting and fine-tuning models. The article concludes by emphasizing the importance of accurate and reliable LLM evaluators in unit-testing LLM applications and introduces DeepEval as a platform that offers a comprehensive solution for evaluating and testing LLMs.