LLM testing is the process of evaluating an LLM output to ensure it meets specific assessment criteria based on its intended application purpose. It is a complicated process due to the nature of black-box models, but concepts from traditional software testing carry over. LLM testing involves unit testing, functional testing, performance testing, responsibility testing, and regression testing. Unit tests evaluate an LLM response for a given input based on clearly defined criteria. Functional testing assesses the model's proficiency across a range of inputs within a particular task. Performance testing optimizes for cost and latency. Responsibility testing evaluates LLM outputs on Responsible AI metrics such as bias, toxicity, and fairness. DeepEval offers a framework to carry out these tests, including automated testing in CI/CD pipelines. Robust LLM evaluation metrics are crucial for determining test pass or fail, and best practices include structuring tests with unit tests, functional tests, performance tests, responsibility tests, and regression tests.