The text discusses the challenges of integrating large language models (LLMs) like OpenAI's into production applications, primarily due to their probabilistic nature and the lack of traditional testing methods such as code coverage. It introduces a tutorial on using automated tests to ensure quality in LLM applications, specifically through a command-line app that answers questions about a book database using LLMs. The tutorial involves configuring prompts using AIConfig and setting up a continuous integration (CI) pipeline with CircleCI to run tests and ensure changes do not degrade performance. By employing both standard string-matching tests and more complex evaluations specific to LLMs, the workflow aims to blend traditional software testing with machine learning model evaluation, offering a robust framework for developing and refining AI-powered applications.