In the world of AI, context is king—a phrase that has gained even more relevance with the rise of Large Language Models (LLMs). While LLMs are evolving rapidly, one major limitation is that externally trained models often lag in knowledge regarding recent events and specific business data. However, there are ways to enhance these models with additional context to deliver results that are custom-tailored to your specific needs.

In this session, we’ll explore how to achieve this by using a combination of well-known Java stacks and libraries, such as Spring AI and LangChain4j. You will learn how to integrate these tools into your Java applications to enrich LLM outputs with business-specific context, allowing you to build smarter and more relevant AI-powered applications.

Join us to discover how to leverage Java’s rich ecosystem to enhance AI models and deliver context-aware solutions for your business.

Target Audience: This session is primarily designed for Java Developers and Back-end Engineers who are interested in integrating AI capabilities into their applications. Software Architects will also find value in understanding how to apply Java-based AI tools to build more intelligent and context-aware systems. AI Enthusiasts and Tech Leads working on enterprise-level AI projects can benefit from learning how to enhance LLMs with business-specific data using Java libraries.