Build AI-Driven Apps with Java: Spring AI, RAG, Agents, MCP

The future of software isn't AI OR Java. It's AI inside Java (Spring Boot).

Every enterprise is racing to put intelligence into its products: chatbots that know company data, assistants that take real actions, and systems that reason, search, and respond in real time. Most of those products run on the JVM, and most AI courses are taught in Python.

This course closes that gap. You'll learn to build AI-powered applications with Spring AI 2.0, Spring Boot 4, and Java, using the same patterns production teams use to ship reliable, observable, and secure AI features.

Production-ready, not tutorial-ready.

This isn't a "call an API and print the response" course. You'll go deep on the parts most courses skip, especially the ones that separate a demo from a real product:

• Spring AI Advisors, the interception layer behind logging, memory, RAG, and guardrails. Most courses barely touch them; here you'll build your own.

• Streaming responses with Server-Sent Events and Reactor Flux, so your UI feels instant.

• Structured output that turns LLM text into type-safe Java objects.

• Retrieval-Augmented Generation (RAG) from ingestion to production, grounded in your own data.

• Tool calling and AI agents that can take real actions inside your system.

• Model Context Protocol (MCP), the emerging standard for connecting AI to tools and data.

What you'll build

You'll start with your first ChatClient call and progress to a complete AI-powered application: a streaming chatbot backend with memory, semantic search, RAG over your own documents, and tool-using agents, connected to a ready-made frontend you can download and run.

Model-agnostic by design

You'll work with OpenAI, Anthropic Claude, Google Gemini, and local open-source models via Ollama, and learn how to swap providers without rewriting your application. That flexibility matters when your company changes vendors, costs, or compliance rules.

What's inside

• LLM fundamentals explained for Java developers (tokens, context windows, temperature, and more)

• Spring AI ChatClient, prompt templates, and prompt chaining

• Structured output with BeanOutputConverter, ListOutputConverter, and MapOutputConverter

• Streaming AI responses with SSE, WebFlux, and Flux

• The Spring AI advisor chain, plus custom advisors for timing, logging, and more

• Chat memory for multi-turn conversations

• Embeddings, vector stores, and semantic search with pgvector

• A complete RAG section covering production concerns

• Tool calling and function calling in Java

• Building AI agents with Spring AI

• MCP for Java developers

• LangChain4j essentials, so you understand the other major Java AI framework

• A capstone: a full-stack streaming AI chatbot

Who's teaching you

I'm Faisal, founder of EmbarkX. Thousands of developers have learned Java, Spring Boot, microservices, and cloud-native engineering through EmbarkX courses. This course brings that same production-first approach to AI engineering.

Why now?

AI engineering is quickly becoming a core expectation for backend developers. Java developers who can integrate LLMs, RAG, and agents into Spring applications are in a rare position: they already know how to build systems enterprises trust. This course gives you the AI half.

Enroll now and start building the next generation of intelligent Java applications.