RAG (Retrieval-Augmented Generation) in Java

While Large Language Models (LLMs) are powerful, they often lack domain-specific knowledge and may produce hallucinations. Retrieval-Augmented Generation (RAG) bridges this gap by allowing AI systems to reference trusted, up-to-date data sources when generating responses.

This talk will take Java developers from RAG fundamentals to a fully functional enterprise-grade RAG implementation using JVM tools and frameworks.

Key Topics Covered:

• Understanding RAG
• Implementing RAG in Java
• Optimizing RAG for Enterprises
• Security and Governance

Objectives:
Teach attendees to design and implement RAG pipelines in Java for domain-specific AI applications. Demonstrate how to connect AI to live, private data sources while maintaining security and compliance.

Target Audience:
Java developers, architects, and AI engineers building intelligent assistants, search tools, or domain-specific AI solutions.

Demo:
The session will feature a live demo of a Java-based RAG application using LangChain4j and a vector database to answer questions from private PDF and database content.