Published 1/2026
Created by Infiproton Tech, Harish B N
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 48 Lectures ( 3h 50m ) | Size: 3 GB
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 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.
Password/解压密码www.tbtos.com
转载请注明：0daytown » Spring AI + RAG: Build Production-Grade AI with Your Data