Spring AI: Build AI Apps with Java, SpringBoot & LLMs

About this course

Generative AI is changing the way modern software is built, but many Java and Spring Boot developers still feel that AI belongs mainly to Python, Machine Learning, or Data Science teams.

This course is designed to change that.

In this course, you will learn how to build practical AI-powered backend applications using Java, Spring Boot, and Spring AI. You do not need prior experience with Machine Learning, Python, or complex math. The goal is to help you use your existing backend engineering skills and extend them into the world of AI Engineering.

We will start from the fundamentals: what LLMs are, how they work, what tokens and context windows mean, and how to choose the right model for your application. Then we will move into real Spring AI implementation, where you will connect your Spring Boot application to AI models such as OpenAI and local models through Ollama.

You will learn how to use Spring AI concepts like ChatModel, ChatClient, prompts, system messages, prompt templates, chat options, response metadata, and structured outputs. You will also understand how to design prompts that are reliable, reusable, and suitable for production applications.

After that, we will explore more advanced AI application patterns such as Retrieval-Augmented Generation, conversational memory, and tool calling. These techniques allow your AI application to answer from your own documents, remember conversation context, and connect to APIs, databases, and business logic.

This course is built for backend developers who want to build real AI features, not just toy demos. By the end of the course, you will understand how to think like an AI Engineer while still working inside the Java and Spring ecosystem you already know.

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