“This course contains the use of artificial intelligence”

The demand for intelligent AI applications has exploded, but most Retrieval-Augmented Generation (RAG) systems fail when deployed in production. Basic implementations often suffer from poor retrieval quality, hallucinations, limited context understanding, and scalability challenges. This course is designed to take you beyond introductory RAG concepts and teach you how to build production-ready RAG systems used in modern enterprises.

In this comprehensive masterclass, you will learn how to design and implement advanced retrieval architectures, including Corrective RAG (CRAG), Self-RAG, Agentic RAG, and Adaptive RAG. You'll explore how leading organizations build reliable AI applications by combining large language models (LLMs) with intelligent retrieval pipelines, robust evaluation frameworks, and scalable infrastructure.

We begin by understanding why traditional RAG implementations fail and how to architect modern solutions from the ground up. You will master advanced techniques such as semantic chunking, parent-child retrieval, sliding window strategies, and context preservation to improve retrieval accuracy and response quality. From there, you'll dive into Hybrid Search, combining dense vector retrieval with BM25, and learn how to optimize results using cross-encoder re-ranking, query expansion, and Hypothetical Document Embeddings (HyDE).

Next, you'll build next-generation systems using Graph RAG, enabling AI applications to reason over knowledge graphs, entities, and relationships. You'll also explore Agentic RAG and Multi-Agent Systems, where AI agents collaborate, plan, invoke tools, and autonomously retrieve information to solve complex tasks. In addition, you'll learn how to develop Multi-Modal RAG applications capable of understanding PDFs, images, audio, video, and structured datasets within a unified knowledge platform.

Production deployment is a major focus of this course. You'll learn how to evaluate RAG systems using industry-standard metrics such as precision, recall, faithfulness, context relevance, and latency benchmarks. We'll also cover distributed vector databases, caching strategies, performance tuning, monitoring, security, and governance to ensure your applications are ready for enterprise environments.

Throughout the course, you'll complete five hands-on projects, including building a Hybrid Search RAG system, implementing Graph RAG with Knowledge Graphs, creating an Agentic AI Assistant, developing a Multi-Modal PDF and Image RAG application, and evaluating a production pipeline using real-world techniques.

By the end of this course, you will have the skills and confidence to design, build, evaluate, scale, and deploy advanced AI systems powered by modern RAG architectures. Whether you're a software engineer, AI developer, machine learning practitioner, data scientist, or technical leader, this course will provide the practical knowledge needed to build the next generation of enterprise AI applications.