Spring AI RAG system design covering ingestion, chunking, retrieval, and prompt reliability.

What you'll learn
- Design end-to-end RAG systems using Spring AI, following backend system design principles rather than demo-style implementations.
- Build repeatable ingestion pipelines for PDFs, wiki documents, and database content with clear structure and metadata.
- Implement effective chunking and embedding pipelines that directly impact retrieval quality and correctness.
- Design metadata-aware retrieval pipelines and integrate them cleanly into backend chat flows.
- Control LLM behavior using explicit prompt orchestration, grounding rules, and source-aware answers.
- Manage the full knowledge lifecycle by safely adding, updating, and deleting data without corrupting retrieval results.

Requirements
- Basic experience with Java and Spring Boot (REST APIs, configuration, project structure).
- Comfortable working with databases and general backend application concepts.
- Familiarity with IDE-based development and running applications locally.
- No prior AI, RAG, or Spring AI experience required — all AI concepts are covered from scratch.

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 Datateaches you how todesign, build, and operate a real Retrieval-Augmented Generation (RAG) systemthe way backend engineers build serious systems — with clear boundaries, explicit pipelines, and production-minded decisions.

This isnota prompt-engineering or chatbot tutorial.It is abackend-first system design coursefocused on correctness, reliability, and long-term maintainability.

You will build a completeInternal Knowledge Assistantfor 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 asystem, not a prompt trick

Ingestion, chunking, retrieval, and prompting areseparate, testable pipelines

Metadata is afirst-class concern, not an afterthought

Knowledge can beadded, updated, and deleted safely

Everything is implemented usingSpring 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 toown 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 aprogressive 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, andcitation-based, source-attributed answers.

Module 8 — Knowledge LifecycleSafe 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 themental model and engineering disciplineneeded to build AI systems that last.

Who this course is for:
- Java and Spring Boot developers who want to integrate RAG into backend applications
- Backend engineers adding AI capabilities to existing systems and services
- Developers who care about system design, correctness, and long-term maintainability
- Engineers who want to understand how RAG works end-to-end, from ingestion to retrieval and controlled generation
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