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Spring AI + RAG: Build Production-Grade AI with Your Data
Published 1/2026
Duration: 3h 50m | .MP4 1920x1080 30fps(r) | AAC, 44100Hz, 2ch | 2.95 GB
Genre: eLearning | Language: English
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
More Info
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https://ddownload.com/7em4lypcglh0/spring.ai.%2B.rag..build.productiongrade.ai.with.your.data.part1.rar
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