Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents

About this course

Want to build real-world Generative AI (GenAI) applications with Java, Spring Boot, Spring AI, RAG and AI Agents—not just experiment with prompts?

This course will take you from fundamentals to production-ready AI systems, including RAG pipelines, AI agents, tool calling, chat memory, MCP, observability, prompt engineering, and prompt hacking.

Hi there! My name is Ali Gelenler. I'm here to help you learn GenAI using Java and Spring AI from fundamentals to real-world production ready AI architectures and systems with a practical approach.

Even if you are not a daily Java developer, this course can still help you understand how production-ready GenAI systems are designed and implemented in a structured backend environment.

In this course, you will focus on creating AI applications to go beyond AI generated code and implement over 20 use cases using Java and Spring AI together with various AI providers and models, such as Open AI, Google Gemini Vertex AI, Hugging Face, Ollama and Docker Model Runner. You will build AI applications and AI systems using LLMs (Large Language Models), integrate vector databases and embeddings, and design scalable backend architectures for Generative AI.The course also includes production-ready Agentic AI design with tool calling, workflow design, chaining, decision control, human-in-the-loop steps, and MCP integration.

You will learn:

• Building end-to-end GenAI systems in Java and Spring AI with advanced Spring AI concepts

Designing RAG pipelines with vector databases, embeddings, similarity search and semantic search using advanced ingestion and retrieval strategies such as query transformer, query expand

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