Most RAG courses stop at loading a few documents and asking questions.
This course goes further.
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.
Includes free 90-day access to IntelliJ IDEA Ultimate for a professional development experience.
Includes professionally prepared subtitles in Spanish, Portuguese (Brazil), Japanese, and Chinese.
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:
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Spring Boot
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Spring AI
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PostgreSQL
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Redis / vector stores
The same codebase evolves throughout the course, exactly like a real backend system.
What Makes This Course Different
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RAG is treated as a system, not a prompt trick
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Ingestion, chunking, retrieval, and prompting are separate, testable pipelines
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Metadata is a first-class concern, not an afterthought
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Knowledge can be added, updated, and deleted safely
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Everything is implemented using Spring AI abstractions, not custom hacks
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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
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How to design ingestion pipelines for PDFs, Markdown, and databases
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Why chunking strategies directly affect retrieval quality
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How embeddings and vector stores fit into backend architecture
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How to build metadata-aware retrieval pipelines
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How to control LLM behavior with explicit prompt orchestration
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How to manage knowledge lifecycle: add, update, delete
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How to build RAG systems that remain correct as data changes
Course Modules Overview
This course is organized as a progressive backend system build, where each module introduces exactly one new system concern.
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Module 1 — Setup & Spring AI Baseline
Spring Boot + Spring AI setup and a minimal chat endpoint to establish the foundation. -
Module 2 — RAG Readiness
Use-case framing, data sources, and infrastructure setup (PostgreSQL, Redis). -
Module 3 — Ingestion Pipelines
Designing repeatable ingestion for PDFs, wiki content, and database records. -
Module 4 — Chunking Strategies
Source-specific chunking approaches and a unified chunking pipeline. -
Module 5 — Embeddings & Vector Storage
Generating embeddings and persisting them with metadata in a vector store. -
Module 6 — Retrieval Pipelines
Metadata-aware similarity search and clean retrieval integration into chat. -
Module 7 — Prompt Orchestration & Reliability
Grounded prompts, explicit behavior control, and citation-based, source-attributed answers. -
Module 8 — Knowledge Lifecycle
Safe add, update, and delete workflows to keep the system correct over time.
Who This Course Is For
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Java and Spring Boot developers
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Backend engineers integrating AI into real systems
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Developers who already understand REST APIs, databases, and Spring fundamentals
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Engineers who want to move beyond demo-level RAG implementations
Who This Course Is NOT For
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Absolute beginners to Java or Spring
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No-code or prompt-only AI learners
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Frontend-focused developers looking for chatbot-only examples
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Learners expecting quick “load a PDF and chat” style examples
Outcome
After completing this course, you will be able to:
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Design RAG systems confidently
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Build production-grade AI pipelines using Spring AI
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Reason about correctness, reliability, and system boundaries
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Apply the same architecture to other real-world use-cases
This course gives you the mental model and engineering discipline needed to build AI systems that last.
