We have newly launched the Practical LLM Service Development for Spring Developers (Part 1) course. Although it may feel a little overdue, this course covers what you need to learn to develop LLM applications using the Spring framework, which is familiar to Java developers. We hope you will take a great interest in it.
Why did I create this course?
These days, keywords like RAG, vector search, and AI agents can be found everywhere, but there aren’t many resources that address, at a practical level, “how to integrate them with a Spring Boot backend.”
This course incorporates into its curriculum the problems I encountered while debugging an actual RAG recommendation endpoint—including how chunking documents by token broke their semantic units and degraded search quality, and how I solved the problem by switching to paragraph-based chunking. Rather than simply showing you the correct code like a tutorial, we’ll follow along together to understand “why it doesn’t work → how to diagnose it → how to fix it.”
Recommended for the following people
Backend developers with Spring Boot experience who are new to developing LLM/AI services
Developers who want to apply RAG and vector search at the level of real-world projects, not just tutorials
Professionals who need to directly implement internal AI features (chatbots, document search, recommendations, etc.) using Spring.
Experience creating REST APIs with Spring Boot is all you need. No prior knowledge of AI/LLMs is required.
What will you learn?
1. Spring AI Core Fundamentals Basic usage of ChatClient, structured output, and streaming response implementation
2. Building a RAG Pipeline Vector store integration, comparison of document chunking strategies and hands-on troubleshooting, search quality improvement, and prompt design
3. Tool and Multimodal Expansion Integrating external tools using MCP (Model Context Protocol), processing multimodal inputs such as images, and integrating with Dify
4. Service Stabilization and Real-World Projects Applying guardrails, designing reliable services, and a mini project that integrates everything you have learned so far
The appendix also covers how to use AI coding tools and Ollama.
Series Guide
This release is Part 1 of the full series. Parts 2 and 3 will cover more advanced architectures and real-world cases, and once the entire series is released, we plan to offer a discount on the full package.
📌 Go to the course: https://inf.run/VVpnV
Feel free to leave any questions or inquiries about the curriculum in the comments. Through Part 1, I hope to take the first step with you toward integrating an LLM into your Spring service.