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The course explains advanced RAG concepts, showcasing their implementation through LangChain for enhanced practical learning and application.

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naive

naive-SPA

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MultiQuery

RAG-Fusion

Decomposition

SemanticRouting

RoutingClassifier

CrossEncoderReranking

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HyDE

The course explains RAG, which combines information retrieval and generative models. Learners will gain hands-on experience with state-of-the-art technologies throughout the course by implementing RAG systems using LangChain.

The course begins with an introduction to the core principles of RAG. As the course progresses, you’ll explore pre-retrieval optimization techniques, including strategies for optimizing indexing and query formulation, as these techniques are covered in depth. You will then focus on post-retrieval optimization, learning about reranking methods like RAG-Fusion and Cross Encoder reranking. 

Finally, you will work on a hands-on project to build an RAG application by applying the concepts learned in the course. By the end of this course, you’ll have gained sufficient experience and knowledge to build more intelligent systems using RAG.

Advanced RAG Techniques: Choosing the Right Approach

Explore the chapter’s structure to learn about post-retrieval techniques in Advanced RAG systems for better response generation.

What Is Post-Retrieval Optimization?

Getting Started

Introduction to Retrieval-Augmented Generation (RAG)

Advanced RAG: Pre-Retrieval (Optimizing Indexing)

Advanced RAG: Pre-Retrieval (Optimizing Query)

Advanced RAG: Post-Retrieval Process

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

Conclusion

What Is Post-Retrieval Optimization?