AI is revolutionizing the software landscape. However, for many Java developers, integrating these powerful AI tools into existing enterprise applications or a fresh 1 can feel daunting. In this hands-on session, we'll demystify the process and show you how to build LLM-powered features straight into your Java codebase.
Using JakartaEE and the LangChain4j library, we'll dive deep into Retrieval Augmented Generation (RAG), a cutting-edge method that combines the vast cognition of LLMs with the precision of your own data. We'll research how to make both few-shot and zero-shot RAG models, and then add applicable 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 supply applicable insights into the architecture and code.
Whether you're acquainted with AI or just getting started, this session will give you the assurance and skills to harness the possible of LLMs in your Java projects.
