About This Course
<div>The future of software isn't AI OR Java. It's AI inside Java.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>Production-ready, not tutorial-ready.</div><div><br></div><div>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:</div><div><ul><li>Spring AI Advisors, the interception layer behind logging, memory, RAG, and guardrails. Most courses barely touch them; here you'll build your own.</li><li><span style="font-size: 1rem;">Streaming responses with Server-Sent Events and Reactor Flux, so your UI feels instant.</span></li><li><span style="font-size: 1rem;">Structured output that turns LLM text into type-safe Java objects.</span></li><li><span style="font-size: 1rem;">Retrieval-Augmented Generation (RAG) from ingestion to production, grounded in your own data.</span></li><li><span style="font-size: 1rem;">Tool calling and AI agents that can take real actions inside your system.</span></li><li><span style="font-size: 1rem;">Model Context Protocol (MCP), the emerging standard for connecting AI to tools and data.</span></li></ul></div><div><span style="font-size: 1rem;">What you'll build</span></div><div><br></div><div>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.</div><div><br></div><div>Model-agnostic by design</div><div><br></div><div>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.</div><div><br></div><div>What's inside</div><div><ul><li>LLM fundamentals explained for Java developers (tokens, context windows, temperature, and more)</li><li><span style="font-size: 1rem;">Spring AI ChatClient, prompt templates, and prompt chaining</span></li><li><span style="font-size: 1rem;">Structured output with BeanOutputConverter, ListOutputConverter, and MapOutputConverter</span></li><li><span style="font-size: 1rem;">Streaming AI responses with SSE, WebFlux, and Flux</span></li><li><span style="font-size: 1rem;">The Spring AI advisor chain, plus custom advisors for timing, logging, and more</span></li><li><span style="font-size: 1rem;">Chat memory for multi-turn conversations</span></li><li><span style="font-size: 1rem;">Embeddings, vector stores, and semantic search with pgvector</span></li><li><span style="font-size: 1rem;">A complete RAG section covering production concerns</span></li><li><span style="font-size: 1rem;">Tool calling and function calling in Java</span></li><li><span style="font-size: 1rem;">Building AI agents with Spring AI</span></li><li><span style="font-size: 1rem;">MCP for Java developers</span></li><li><span style="font-size: 1rem;">LangChain4j essentials, so you understand the other major Java AI framework</span></li><li><span style="font-size: 1rem;">A capstone: a full-stack streaming AI chatbot</span></li></ul></div><div><span style="font-size: 1rem;">Who's teaching you</span></div><div><br></div><div>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.</div><div><br></div><div>Why now?</div><div><br></div><div>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.</div><div><br></div><div>Enroll now and start building the next generation of intelligent Java applications.</div>
What you'll learn:
- Build AI-powered Java applications with Spring AI and Spring Boot 4 using production-ready patterns
- Integrate OpenAI, Anthropic Claude, Google Gemini and local Ollama models, and switch providers without rewriting code
- Master the Spring AI ChatClient, prompt templates and prompt chaining for reliable LLM interactions
- Convert LLM responses into type-safe Java objects using Spring AI structured output converters
- Stream real-time AI responses to the browser using Server-Sent Events, WebFlux and Reactor Flux
- Build custom Spring AI Advisors for logging, timing, memory and guardrails across the advisor chain
- Implement semantic search with embeddings and pgvector as one complete generate, store and search workflow
- Build production-grade RAG pipelines that ground AI answers in your own documents and data
- Create AI agents with tool calling that take real actions inside your Spring Boot applications
- Connect Java apps to external tools and data using the Model Context Protocol (MCP)
- Understand LangChain4j and when to choose it alongside Spring AI
- Ship a full-stack streaming AI chatbot as a portfolio-ready capstone project