inflearn logo
inflearn logo

Spring AI + RAG: Build Production-Grade AI with Your Data -(2026)

Course Summary: This course teaches Java and Spring Boot developers how to design, build, and maintain Retrieval-Augmented Generation (RAG) systems at production level. Using Spring AI, PostgreSQL, and Redis, you will learn how to create robust pipelines for ingestion, chunking, embeddings, and retrieval, while orchestrating LLM behavior with reliable prompts. I share my backend engineering experience to help students solve common challenges: data inconsistency, inefficient retrieval, and unreliable prompts. By the end, you will be able to build a fully functional and maintainable internal knowledge assistant, with scalable, production-ready code.

강의소개.상단개요.수강생

난이도 초급

수강기한 무제한

Java
Java
PostgreSQL
PostgreSQL
Spring Boot
Spring Boot
RAG
RAG
Java
Java
PostgreSQL
PostgreSQL
Spring Boot
Spring Boot
RAG
RAG

강의상세_배울수있는것_타이틀

  • Design reliable, reusable ingestion pipelines for PDFs, Markdown documents, and databases

  • Implement chunking and embedding strategies that improve retrieval quality

  • Build metadata-aware retrieval pipelines integrated into backend chat flows

  • Orchestrate and control LLM behavior with contextual, source-aware prompts

  • Manage the knowledge lifecycle: safely add, update, and delete information

Build Production-Grade AI with Spring AI + RAG

Learn how to design, build, and maintain real-world RAG (Retrieval-Augmented Generation) systems using Spring AI, PostgreSQL, and Redis. This course teaches backend-first AI engineering for developers who want production-ready, maintainable systems—far beyond demo-style chatbots. Ideal for Java and Spring Boot developers integrating AI into enterprise systems.

I built this course to help backend developers who struggle with unreliable AI pipelines. Most RAG tutorials focus on demos, but real systems need structure, metadata-awareness, and safe knowledge updates. This course reflects years of experience in production backend AI systems.

What You’ll Learn

Section 1: Core Keywords

Students will learn:

  • How to design repeatable ingestion pipelines for PDFs, Markdown, and databases

  • How to implement chunking strategies that improve retrieval accuracy

  • How to build embeddings and vector storage pipelines integrated with metadata

  • How to orchestrate prompt behavior for LLMs, including grounding rules and source attribution

Section 2: Core Keywords

Students will continue learning:

  • How to create metadata-aware retrieval pipelines integrated with backend chat flows

  • How to manage the knowledge lifecycle: adding, updating, deleting data safely

  • How to validate retrieval pipelines and ensure correctness as data changes

  • Best practices for building production-grade AI systems that scale and maintain reliability

Before You Enroll

Prerequisites & Notices:

  • Basic knowledge of Java and Spring Boot (REST APIs, project structure)

  • Comfortable with databases and backend application concepts

  • No prior AI, RAG, or Spring AI experience required

  • Recommended: IDE-based development and running applications locally

Audio / Video Quality & Study Tips:

  • All videos are high-quality with closed captions

  • Follow along by coding the examples and running pipelines locally

  • Pause and experiment with the exercises to reinforce understanding

Questions & Updates:

  • Students can ask questions in the Udemy Q&A

  • The course will receive updates for Spring AI version changes

Disclaimer:

  • All course materials, code, and diagrams are original or properly licensed


강의소개.콘텐츠.추천문구

학습 대상은 누구일까요?

  • Java / Spring Boot developers who want to integrate AI into backend applications reliably

  • Backend engineers frustrated with “demo-only” RAG solutions that are not maintainable or production-ready

  • Developers who want to understand how to build production-grade AI systems rather than just using libraries or prompts

선수 지식, 필요할까요?

  • Basic experience with Java and Spring Boot (REST APIs, configuration, project structure).

  • Comfortable working with databases and general backend concepts

  • Familiarity with IDE-based development and running applications locally.

  • No prior experience required: No AI, RAG, or Spring AI knowledge is necessary

강의소개.지공자소개

78

수강생

4

수강평

1

답변

5.0

강의 평점

22

강의_other

I have been actively using Blender for the last 4 years, mainly for creating film animation. In my courses you can learn a lot about modeling, texturing, lighting creation, post-processing and animation. My goal is to achieve as realistic a render as possible. All my courses are step-by-step and intended for users who have no previous experience in Blender.

더보기

커리큘럼

전체

47개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

해당 강의에서 제공: [object Object]
강의 게시일: 
마지막 업데이트일: 

수강평

아직 충분한 평가를 받지 못한 강의입니다.
모두에게 도움이 되는 수강평의 주인공이 되어주세요!

Sime Bugarija님의 다른 강의

지식공유자님의 다른 강의를 만나보세요!

비슷한 강의

같은 분야의 다른 강의를 만나보세요!

강의상세.할인문구

$35,420.00

30%

$38.50