Interested in adding AI capabilities to your Java applications? LangChain4j makes it simple to integrate large language models (LLMs) directly into your existing codebase without leaving the Java ecosystem.

In this session, we will go beyond “Hello World” examples and explore real-world use cases where AI can enhance everyday applications, from intelligent assistants and document summarizers to data enrichment and natural language interfaces. Through live coding and practical demos, you will learn how to connect to models, chain prompts, manage context, and integrate AI workflows into Spring or Micronaut applications.

By the end, you will have a clear understanding of how to design, implement, and extend AI-powered features in Java using LangChain4j.

What You Will Learn

• How LangChain4j bridges Java and large language models

• Practical examples of integrating AI features into real applications

• How to build, chain, and orchestrate AI prompts and tools

• Best practices for managing context, inputs, and outputs

• How to extend LangChain4j with custom tools and connectors

Who Should Attend

Java developers and architects looking to apply AI practically in production applications using familiar frameworks and tools.