Advanced RAG with Spring AI: Enterprise AI Masterclass

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.