Java Meets AI: Empowering Spring Developers to Build Intelligent Apps

Enterprise Java is ready for generative AI. Learn to build intelligent apps with Spring AI, using advanced patterns like RAG and Tool Calling.

#1about 2 minutes

Understanding the fundamentals of generative AI for developers

Learn the core concepts of generative AI, including foundation models, large language models (LLMs), prompts, and the token-based prediction process.

#2about 4 minutes

The role of frameworks in simplifying AI integration

Discover why frameworks are crucial for integrating AI, providing high-level abstractions for REST APIs, structured outputs, and easy model switching.

#3about 3 minutes

A comparison of popular Java AI frameworks

Get an overview of the Java AI ecosystem, comparing the key features and origins of LangChain4j, Spring AI, and Microsoft's Semantic Kernel.

#4about 7 minutes

Building an AI application using LangChain4j

See a practical implementation of an AI-powered recipe finder using LangChain4j, from low-level models to the high-level, declarative AI Service abstraction.

#5about 4 minutes

Implementing the same AI application with Spring AI

Explore how to build the same recipe finder application using Spring AI's fluent ChatClient API for a streamlined, builder-pattern approach to AI calls.

#6about 5 minutes

Advanced patterns for building sophisticated AI applications

Understand common LLM limitations like context size and lack of custom knowledge, and learn about advanced patterns like RAG and tool calling to solve them.

#7about 3 minutes

Implementing RAG and tool calling with LangChain4j

Learn how to implement retrieval-augmented generation (RAG) and tool calling in LangChain4j using its built-in abstractions like the EmbeddingStoreIngester and @Tool annotation.

#8about 1 minute

Implementing RAG and tool calling with Spring AI

Discover how Spring AI handles advanced patterns by using the ChatClient's fluent API for tool registration and the Advisor concept for implementing RAG.

#9about 2 minutes

Exploring AI agents and the Model Context Protocol

Get a glimpse into the future of autonomous AI agents and how the Model Context Protocol (MCP) aims to standardize interactions between different AI services.