Building AI powered applications with Java

This session explores enhancing AI models with additional context to tailor results to specific needs, addressing the knowledge gap of externally trained LLMs. It demonstrates using Java stacks and libraries like Spring AI and LangChain4j to achieve this goal.

Andres AlmirayOracle

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Thursday, June 12, 14:55-15:45

Room 1

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It's said that context is king. This phrase has become more important in recent times thanks to the rise of AI and LLMs. LLMs are evolving faster and faster every month. However, trained models lag in knowledge regarding recent events as well as specific data pertaining to your business, specially those models trained externally. This being said, there are ways to enhance these models with additional context enabling them to deliver results custom tailored to your specific needs.In this session we'll have a look at how we can achieve this goal using a combination of well known Java stacks and libraries such as Spring AI and LangChain4j.

llms

context

java

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Andres Almiray

Oracle

Switzerland

Andres is a Java/Groovy developer and a Java Champion with more than 20 years of experience in software design and development. He has been involved in web and desktop application development since the early days of Java. Andres is a true believer in open source and has participated on popular projects like Groovy, Griffon, and DbUnit, as well as starting his own projects. Founding member of the Griffon framework and Hackergarten community event. Creator of JReleaser.

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