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 buildBy the end of this course, you’ll have built an advanced enterprise RAG application featuring:Enterprise knowledge ingestion pipelinesMultiple document chunking strategiesVector embeddings and PostgreSQL with pgvectorSemantic search and Hybrid RetrievalRetrieval ranking and re-rankingQuery rewriting and Multi-Query RetrievalPrompt orchestration and grounded response generationRetrieval evaluation and benchmark frameworksSpring Boot Actuator and Prometheus monitoringMetadata filtering and multi-tenant retrievalAudit logging and PII-aware retrievalFreshness-aware ranking and response cachingSelf-RAGCorrective RAGAdaptive RAGA practical introduction to GraphRAG using Neo4jEnterprise-ready RAG architecture and best practicesWhat you’ll learnThroughout the course you’ll learn how to:Build enterprise-grade RAG systems using Spring AIDesign scalable ingestion and indexing pipelinesImprove retrieval quality using Hybrid Search and advanced ranking techniquesOptimize prompts for grounded LLM responsesEvaluate retrieval accuracy and answer qualityMeasure latency, benchmark retrieval performance, and monitor production systemsSecure enterprise AI applications with metadata filtering, tenant isolation, audit logging, and PII protectionImplement modern RAG architectures including Self-RAG, Corrective RAG, Adaptive RAG, and GraphRAGUnderstand when each retrieval strategy should be used in real-world enterprise applicationsWhy 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.