Spring AI + RAG: Build Production-Grade AI with Your Data
Course Description
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.
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
Course Modules Overview
This course is organized as a progressive backend system build, where each module introduces exactly one new system concern.
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
Java and Spring Boot developers
Backend engineers integrating AI into real systems
Developers who already understand REST APIs, databases, and Spring fundamentals
Engineers who want to move beyond demo-level RAG implementations
Who This Course Is NOT For
Absolute beginners to Java or Spring
No-code or prompt-only AI learners
Frontend-focused developers looking for chatbot-only examples
Learners expecting quick "load a PDF and chat" style examples
Outcome
After completing this course, you will be able to:
Design RAG systems confidently
Build production-grade AI pipelines using Spring AI
Reason about correctness, reliability, and system boundaries
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.
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