The tutorial explores the process of building and automating custom language model-powered applications using tools like LangChain and LangSmith within a continuous integration and continuous delivery (CI/CD) framework. It highlights the challenges of using large language models (LLMs) for enterprise applications and addresses how retrieval augmented generation (RAG) can enhance model accuracy by supplying relevant data at execution time. The guide demonstrates setting up a CircleCI pipeline to automate testing and evaluation, particularly focusing on the integration of LangChain's RAG application with LangSmith for debugging and testing. The example provided includes creating an automated question-answering chatbot that leverages LangChain’s functionalities to efficiently manage and test LLM applications. The tutorial emphasizes the importance of standardizing and automating workflows to improve the scalability and reliability of AI development processes, offering a detailed walkthrough for setting up the necessary environment, tools, and tests to achieve these goals.