Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” Java application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now? Luckily, we can infuse AI models built by actual AI experts into our applications using new open source projects. We promise it’s not as complicated as you might think! Thanks to the ease of use and superb developer experience of Quarkus and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy. In this lab, we’ll start from the Quarkus DevUI where you can try out AI models even before writing any code. Then we’ll get our hands dirty with some code and exploring LangChain4j features such as prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your documents using retrieval augmented generation (RAG); and discovering ways to run (and train) models locally using tools like Ollama and/or Podman AI Lab. you'll also learn the observability and fault tolerance of the AI integration and compile the app to a native binary. Come to this lab to learn how to build AI-infused applications with Quarkus experts working on the Quarkus LangChain4j extensions.