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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.

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MultiQuery

RAG-Fusion

Decomposition

SemanticRouting

RoutingClassifier

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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

What is the main limitation addressed by RAG for large language models (LLMs)?

What is the main limitation addressed by RAG for large language models (LLMs)?

Advanced RAG combines retrieval functionalities with large language models (LLMs) to improve the accuracy and relevance of generated responses.

Advanced RAG can only be used for text-based data.

Advanced RAG leverages retrieved data to provide a strong foundation for LLMs to generate informative answers.

Advanced RAG offers various methods to optimize retrieval, such as sentence-window retrieval and re-ranking.

**(Select all that apply.)** Which statement is *true* about Advanced Retrieval-Augmented Generation (Advanced RAG)?

(Select all that apply.) Which statement is true about Advanced Retrieval-Augmented Generation (Advanced RAG)?

Increased ability to answer complex questions

What is *not* a benefit of using RAG models?

What is not a benefit of using RAG models?

To rewrite the user’s query for better retrieval results

To filter retrieved information based on the LLM’s assessment

To merge retrieved passages into a single representation for the LLM

To select the optimal data source for a specific query

What is the primary purpose of RAG-Fusion in advanced RAG?

What is the primary purpose of RAG-Fusion in advanced RAG?

The overall workflow involves retrieval, augmentation, and generation.

The use of pre-retrieval and post-retrieval optimization techniques.

The computational efficiency for processing information retrieval tasks.


The level of customization and flexibility offered for different tasks.

How is modular RAG different from advanced RAG?

How is modular RAG different from advanced RAG?

Test your understanding of RAG and its paradigms.

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

Quiz: Introduction to RAG