Spring AI + RAG: Build Production-Grade AI with Your Data

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 BootSpring AIPostgreSQLRedis / vector storesThe same codebase evolves throughout the course, exactly like a real backend system.

What Makes This Course DifferentRAG is treated as a system, not a prompt trickIngestion, chunking, retrieval, and prompting are separate, testable pipelinesMetadata is a first-class concern, not an afterthoughtKnowledge can be added, updated, and deleted safelyEverything is implemented using Spring AI abstractions, not custom hacksNo Python, no LangChain, no demo-only shortcutsBy 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 LearnHow to design ingestion pipelines for PDFs, Markdown, and databasesWhy chunking strategies directly affect retrieval qualityHow embeddings and vector stores fit into backend architectureHow to build metadata-aware retrieval pipelinesHow to control LLM behavior with explicit prompt orchestrationHow to manage knowledge lifecycle: add, update, deleteHow to build RAG systems that remain correct as data changesCourse Modules OverviewThis course is organized as a progressive backend system build, where each module introduces exactly one new system concern. Module 1 — Setup & Spring AI BaselineSpring Boot + Spring AI setup and a minimal chat endpoint to establish the foundation.

Module 2 — RAG ReadinessUse-case framing, data sources, and infrastructure setup (PostgreSQL, Redis). Module 3 — Ingestion PipelinesDesigning repeatable ingestion for PDFs, wiki content, and database records. Module 4 — Chunking StrategiesSource-specific chunking approaches and a unified chunking pipeline.

Module 5 — Embeddings & Vector StorageGenerating embeddings and persisting them with metadata in a vector store. Module 6 — Retrieval PipelinesMetadata-aware similarity search and clean retrieval integration into chat. Module 7 — Prompt Orchestration & ReliabilityGrounded prompts, explicit behavior control, and citation-based, source-attributed answers.

Module 8 — Knowledge LifecycleSafe add, update, and delete workflows to keep the system correct over time. Who This Course Is ForJava and Spring Boot developersBackend engineers integrating AI into real systemsDevelopers who already understand REST APIs, databases, and Spring fundamentalsEngineers who want to move beyond demo-level RAG implementationsWho This Course Is NOT ForAbsolute beginners to Java or SpringNo-code or prompt-only AI learnersFrontend-focused developers looking for chatbot-only examplesLearners expecting quick "load a PDF and chat" style examplesOutcomeAfter completing this course, you will be able to:Design RAG systems confidentlyBuild production-grade AI pipelines using Spring AIReason about correctness, reliability, and system boundariesApply the same architecture to other real-world use-casesThis course gives you the mental model and engineering discipline needed to build AI systems that last.