Spring AI + RAG: Build Production-Grade AI with Your Data teaches you how to design, build, and operate a real Retrieval-Augmented Generation (RAG) system the way backend engineers build serious systems — with clear boundaries, explicit pipelines, and production-minded decisions.
This is not a prompt-engineering or chatbot tutorial. It is a backend-first system design course focused on correctness, reliability, and long-term maintainability.
You will build a complete Internal Knowledge Assistant for a fictional company, using:
- Spring Boot
- Spring AI
- PostgreSQL
- Redis / vector stores
The same codebase evolves throughout the course, exactly like a real backend system.
What Makes This Course Different:
- RAG is treated as a system, not a prompt trick
- Ingestion, chunking, retrieval, and prompting are separate, testable pipelines
- Metadata is a first-class concern, not an afterthought
- Knowledge can be added, updated, and deleted safely
- Everything is implemented using Spring AI abstractions, not custom hacks
- No Python, no LangChain, no demo-only shortcuts
By the end, you will not just “use Spring AI” — you will understand how to own and evolve an AI system in production.
What You Will Learn:
- How to design ingestion pipelines for PDFs, Markdown, and databases
- Why chunking strategies directly affect retrieval quality
- How embeddings and vector stores fit into backend architecture
- How to build metadata-aware retrieval pipelines
- How to control LLM behavior with explicit prompt orchestration
- How to manage knowledge lifecycle: add, update, delete
- How to build RAG systems that remain correct as data changes
