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Keep shoppingAdvanced RAG with Spring AI: Enterprise AI Masterclass
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Advanced RAG with Spring AI: Enterprise AI Masterclass
Build Hybrid RAG, Self-RAG, Adaptive RAG, Evaluation, GraphRAG, Retrieval Optimization and Enterprise AI Systems
Created byInfiproton Tech, Harish B N
Last updated 7/2026
English
What you'll learn
- Build production-ready enterprise RAG applications using Spring AI, Java, Spring Boot, PostgreSQL, and pgvector.
- Implement Hybrid RAG, Query Rewriting, Multi-Query Retrieval, Re-ranking, and Metadata Filtering to improve retrieval quality.
- Evaluate and optimize RAG systems using retrieval benchmarks, groundedness testing, custom metrics, Actuator, and Prometheus.
- Build Self-RAG, Corrective RAG, Adaptive RAG, and a practical GraphRAG proof of concept using Neo4j and Spring AI.
- Create enterprise ingestion pipelines for PDFs, databases, reports, images, and wiki content to build searchable AI knowledge bases.
- Secure enterprise RAG systems with multi-tenant retrieval, audit logging, PII detection, freshness ranking, and response caching.
- Choose the right RAG architecture for enterprise AI by comparing Standard RAG, Hybrid RAG, Self-RAG, Adaptive RAG, and GraphRAG.
- Build a complete enterprise AI support assistant by integrating retrieval, prompt orchestration, evaluation, and LLM generation.
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Course content
12 sections • 74 lectures • 9h 13m total length
Requirements
- Basic Java programming experience is required.
- Familiarity with Spring Boot and REST APIs is recommended.
- A basic understanding of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) is helpful.
- A computer capable of running Java 17+, Docker, PostgreSQL, Neo4j, and IntelliJ IDEA (Community Edition is sufficient).
- No prior experience with advanced RAG architectures such as Hybrid RAG, Self-RAG, Adaptive RAG, or GraphRAG is required—we'll build them step by step throughout the course.
Description
Retrieval-Augmented Generation (RAG) has become the foundation of modern enterprise AI applications. While basic RAG systems can answer questions using your own data, production-grade enterprise systems require far more than semantic search and prompt engineering.
In this course, you'll move beyond traditional RAG implementations and learn how to build intelligent, production-ready retrieval systems using Spring AI, Java, and Spring Boot.
Rather than focusing on isolated concepts, you'll build a complete enterprise AI platform step by step using a realistic support assistant application. Throughout the course, you'll implement advanced retrieval techniques, optimize search quality, evaluate RAG performance, and explore modern retrieval architectures used in enterprise AI systems.
What you'll build
By the end of this course, you'll have built an advanced enterprise RAG application featuring:
Enterprise knowledge ingestion pipelines
Multiple document chunking strategies
Vector embeddings and PostgreSQL with pgvector
Semantic search and Hybrid Retrieval
Retrieval ranking and re-ranking
Query rewriting and Multi-Query Retrieval
Prompt orchestration and grounded response generation
Retrieval evaluation and benchmark frameworks
Spring Boot Actuator and Prometheus monitoring
Metadata filtering and multi-tenant retrieval
Audit logging and PII-aware retrieval
Freshness-aware ranking and response caching
Self-RAG
Corrective RAG
Adaptive RAG
A practical introduction to GraphRAG using Neo4j
Enterprise-ready RAG architecture and best practices
What you'll learn
Throughout the course you'll learn how to:
Build enterprise-grade RAG systems using Spring AI
Design scalable ingestion and indexing pipelines
Improve retrieval quality using Hybrid Search and advanced ranking techniques
Optimize prompts for grounded LLM responses
Evaluate retrieval accuracy and answer quality
Measure latency, benchmark retrieval performance, and monitor production systems
Secure enterprise AI applications with metadata filtering, tenant isolation, audit logging, and PII protection
Implement modern RAG architectures including Self-RAG, Corrective RAG, Adaptive RAG, and GraphRAG
Understand when each retrieval strategy should be used in real-world enterprise applications
Why take this course?
Many RAG tutorials stop after demonstrating vector search and a simple chatbot. Real enterprise AI systems are significantly more sophisticated.
This course focuses on the techniques used to improve retrieval quality, increase answer reliability, monitor production systems, and build scalable enterprise AI applications. Every concept is demonstrated through practical coding using Spring AI, Java, and Spring Boot, with a strong emphasis on architecture, clean design, and production-oriented implementation.
If you've already built a basic RAG application and want to learn what comes next, this course is designed for you.
Who this course is for:
- Java developers who want to build advanced enterprise AI applications using Spring AI and Retrieval-Augmented Generation (RAG).
- Spring Boot developers looking to move beyond basic RAG and learn production-ready enterprise retrieval techniques.
- Backend engineers who want to implement Hybrid RAG, Self-RAG, Corrective RAG, Adaptive RAG, and GraphRAG in real applications.
- Backend engineers who want to implement Hybrid RAG, Self-RAG, Corrective RAG, Adaptive RAG, and GraphRAG in real applications.
- AI engineers and solution architects who want to understand modern RAG architectures, retrieval optimization, evaluation, and enterprise AI best practices.
- Students who have completed a basic RAG course and are ready to learn advanced retrieval architectures and production techniques.
- This course is not intended for absolute beginners to Java or Spring Boot. A basic understanding of Spring Boot development and RAG concepts is recommended.