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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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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

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Learn how to choose the right RAG approach for your application.

Choosing the Best Approach for Your RAG Application

Paradigm	Description	Pros	Cons	Applications
Naive RAG (Retrieve-Read)	Simplest paradigm with indexing, retrieval, and generation.	Easy to implement, computationally efficient.	Limited control over retrieved information, LLM might struggle with synthesis.	Simple question answering, short document summarization.
Advanced RAG (Retrieve-Read-Rewrite-Rerank)	Builds on Naive RAG with pre-retrieval and post-retrieval processing for improved retrieval quality.	More control over information, improved response relevance.	More complex to implement than Naive RAG.	Complex question answering, longer document summarization.
Modular RAG (Flexible Architecture)	Most versatile paradigm with specialized modules for enhanced retrieval and processing	Highly customizable, allows for experimentation and innovation.	Most complex to implement, requires deeper understanding of individual RAG components.	Domain-specific question answering, tailored creative text generation tasks.

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

Choosing the Best Approach for Your RAG Application

A comparative analysis of RAG paradigms

Choosing the optimal RAG paradigm