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World Congress 2025 • Aug 20, 2025 • Session details

Java Meets AI: Empowering Spring Developers to Build Intelligent Apps

Timo Salm

Generative AI isn't just for Python anymore. Spring AI and LangChain4j eliminate complex boilerplate. Start building intelligent, scalable enterprise Java applications today.

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#1 about 1 min

Overview of enterprise Java and generative AI

The evolving role of generative AI in enterprise software and the Spring ecosystem.

#2 about 2 min

Understanding foundation models and generative AI capabilities

How massive foundation models enable natural language understanding and magical application capabilities.

#3 about 1 min

Processing tokens and probability in large language models

Large language models generate responses by calculating the statistical probability of subsequent text tokens.

#4 about 2 min

Integrating AI models into applications via REST APIs

How Java applications interact with provider models by sending authorized HTTP requests.

#5 about 2 min

Benefits of using AI frameworks for Java development

High-level frameworks handle advanced usage patterns, structured output generation, and seamless local model switching.

#6 about 3 min

Comparing popular Java frameworks for AI integration

An overview of LangChain4j, Spring AI, and Semantic Kernel for enterprise development.

#7 about 3 min

Demonstrating a recipe finder AI application

A practical Spring application uses external models to generate localized recipes and images.

#8 about 5 min

Implementing generative AI features using LangChain4j

Abstracting complex REST interactions and prompt templating through declarative service interfaces and annotations.

#9 about 5 min

Building intelligent interactions with the Spring AI framework

Constructing robust large language model requests using auto-configuration and the fluent chat client API.

#10 about 2 min

Mitigating context window limits with prompt engineering

Refining model inputs to produce targeted responses without relying on extensive fine-tuning.

#11 about 2 min

Extending model capabilities through dynamic tool calling

Permitting language models to request synchronous application function execution for real-time data retrieval.

#12 about 3 min

Providing custom knowledge with retrieval-augmented generation

Translating textual requests into contextual embeddings to query vector databases for relevant localized knowledge.

#13 about 4 min

Configuring document retrieval and functions in Java abstractions

Defining embedder pipelines, chunking strategies, and request interceptors to enrich baseline prompt execution flows.

#14 about 2 min

Standardizing autonomous agent logic with model context protocol

Employing standard client-server patterns to orchestrate autonomous tool decisions and remote service integrations.

Matching moments

2:54 min

Utilizing Java frameworks to interface with AI models

Mary Grygleski Mary Grygleski · LIVE

3:36 min

Overview of generative AI and the presentation agenda

juarezjunior juarezjunior · World Congress 2024

2:55 min

Bridging Java applications and AI models with LangChain4j

Daniel Oh Daniel Oh +1 · World Congress 2025

1:56 min

Consuming generative artificial intelligence models in Java

Kevin Dubois Kevin Dubois · World Congress 2024

2:55 min

Building AI interactions in Java with LangChain4j

Alex Soto Alex Soto · World Congress 2025

1:33 min

Core design principles and capabilities of Spring AI

Sandra Ahlgrimm Sandra Ahlgrimm +1 · World Congress 2024

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