The text discusses the concept of hallucinations in large language models (LLMs), where the models generate incorrect or nonsensical responses due to their probabilistic nature. Addressing these hallucinations is challenging for AI developers as it adds complexity and unpredictability to AI systems. The article outlines how frequent human review and prompt engineering can help, but these methods are not scalable. Instead, it suggests combining LLM evaluations with continuous integration (CI) to automate the detection and resolution of hallucinations. The text provides a tutorial on setting up a CI pipeline using CircleCI to automate the evaluation of LLM responses. It demonstrates how to build an AI-powered quiz generator using OpenAI's ChatGPT, LangChain, and CircleCI, and explains the importance of automated testing in preventing hallucinations from affecting user experience. The tutorial concludes by emphasizing the significance of CI in maintaining the reliability and accuracy of LLM applications and encourages readers to enroll in a course on automated testing for LLMOps offered by Deeplearning.AI.