OPENAI_API_KEY

Learn advanced RAG techniques in this advanced RAG course. Explore pre- and post-retrieval optimization with LangChain, and build intelligent, scalable applications with hands-on projects.

Retrieval_ augmenting_ generation - cover art.png

RAG-Final.tar.gz

naive

naive-SPA

naive-copy

code-widget

MultiQuery

RAG-Fusion

Decomposition

SemanticRouting

RoutingClassifier

CrossEncoderReranking

CrossEncoderReranking-copy

HyDE

Master advanced retrieval-augmented Generation (RAG) techniques in this comprehensive course for experienced learners. You’ll gain hands-on experience implementing advanced RAG systems with LangChain, equipping you to create intelligent, efficient AI applications.

The course begins by revisiting the core principles of RAG and then dives into pre-retrieval optimization techniques, focusing on strategies like indexing and query formulation. Next, you’ll explore post-retrieval optimization methods, such as RAG-Fusion and cross encoder reranking, to refine and enhance retrieved data.

Finally, you’ll work on a practical project to build a fully functional RAG application, applying advanced pre- and post-retrieval optimization techniques. After completing this course, you’ll have the expertise to design intelligent, scalable systems that harness the full potential of RAG.

Advanced RAG Techniques: Choosing the Right Approach

Explore the chapter’s structure to learn about optimizing indexing in Advanced RAG systems for better response generation.

What Is Pre-Retrieval Optimizing Indexing?

Getting Started

Introduction to Retrieval-Augmented Generation (RAG)

Advanced RAG: Pre-Retrieval (Optimizing Indexing)

Advanced RAG: Pre-Retrieval (Optimizing Query)

Build a RAG Using LangChain with Google Gemini

Advanced RAG: Post-Retrieval Process

Talk to Your Web Page: A RAG-Powered Chat Interface

Conclusion

What Is Pre-Retrieval Optimizing Indexing?