Advanced RAG Techniques

About this course

Are you an AI engineer, machine learning professional, data scientist, software developer, or someone looking to build more accurate, reliable, and production-ready Retrieval-Augmented Generation (RAG) applications?

Imagine being able to improve document retrieval, optimize chunking strategies, enhance user queries, combine multiple search methods, build Self-RAG and Corrective RAG systems, work with complex PDFs and tables, retrieve information from multimodal data, and evaluate and optimize RAG applications for real-world use.

Retrieval-Augmented Generation is rapidly becoming one of the most important techniques for building AI applications that can work with private, domain-specific, and constantly changing information. However, building a basic RAG pipeline is only the beginning. Real-world applications require advanced retrieval strategies, better query understanding, reliable architectures, multimodal capabilities, evaluation, cost optimization, and production observability.

Advanced RAG techniques help overcome many of the limitations found in traditional RAG systems. From semantic chunking and hybrid search to query rewriting, HyDE, Self-RAG, Corrective RAG, and GraphRAG, these techniques can significantly improve the quality and reliability of AI-powered applications.

In this course, you will learn how to build and optimize advanced RAG systems through a practical, hands-on approach. Starting with RAG foundations and architecture, you will explore advanced chunking and retrieval techniques, query transformation, advanced RAG architectures, multimodal retrieval, evaluation, cost optimization, and production monitoring.

What You Will LearnUnderstanding RAG architecture and advanced retrieval workflows

Learning different chunking strategies for RAG applications

Implementing fixed-size and semantic chunking techniques

Understanding the limitations of fixed-size chunking

Building seman

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