Elevator Pitch
AI is transforming software. Learn how to integrate LLM-powered features into Java using JakartaEE and LangChain4j. In this live demo, we’ll build an AI-powered online store backend, covering RAG, summarization, and similarity search. Gain practical skills in using LLMs in your Java projects.
Description
AI is revolutionizing the software landscape. However, for many Java developers, integrating these powerful AI tools into existing enterprise applications or a new one can feel daunting. In this hands-on session, we’ll demystify the process and show you how to build LLM-powered features directly into your Java codebase.
Using JakartaEE and the LangChain4j library, we’ll dive deep into Retrieval Augmented Generation (RAG), a cutting-edge technique that combines the vast knowledge of LLMs with the precision of your own data. We’ll explore how to create both few-shot and zero-shot RAG models, and then add practical features like summarization and similarity search, backed by an Embedding database.
Through a live coding demo, we’ll walk you through constructing an AI-powered online store backend and provide practical insights into the architecture and code.
Whether you’re familiar with AI or just getting started, this session will give you the confidence and skills to harness the potential of LLMs in your Java projects.
Notes
This talk is designed specifically for seasoned Java developers who are eager to leverage AI technologies without getting lost in the hype. It offers practical insights on integrating LLMs into JakartaEE-based applications, focusing on real-world implementation rather than buzzwords. While the content spans multiple categories—Core Java, Java Platform, Jakarta EE, and AI—the focus is firmly on Jakarta EE, providing a solid foundation for developers looking to enhance their enterprise applications with cutting-edge AI capabilities.