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Advanced RAG Techniques: Choosing the Right Approach
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.
4.6
28 Lessons
4h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- A deep understanding of retrieval-augmented generation (RAG) and its ability to enhance the accuracy of large language models
- Mastery of advanced RAG implementation approaches and the ability to choose the right method for specific applications
- Hands-on experience building RAG systems using LangChain
- Proficiency in pre-retrieval optimization techniques, such as indexing and query formulation
- An understanding of post-retrieval optimization methods, including RAG-Fusion and Cross Encoder reranking
- The ability to design and implement RAG-based chatbots and other intelligent systems
Learning Roadmap
1.
Getting Started
Getting Started
Get familiar with RAG to enhance factual accuracy and build robust NLP applications.
2.
Introduction to Retrieval-Augmented Generation (RAG)
Introduction to Retrieval-Augmented Generation (RAG)
Look at the integration of retrieval-based and generative models for enhanced NLP tasks.
3.
Advanced RAG: Pre-Retrieval (Optimizing Indexing)
Advanced RAG: Pre-Retrieval (Optimizing Indexing)
7 Lessons
7 Lessons
Break apart strategies to optimize indexing, chunking, and data cleaning for efficient retrieval.
4.
Advanced RAG: Pre-Retrieval (Optimizing Query)
Advanced RAG: Pre-Retrieval (Optimizing Query)
8 Lessons
8 Lessons
Break down complex ideas in pre-retrieval query optimization for enhanced RAG accuracy.
5.
Advanced RAG: Post-Retrieval Process
Advanced RAG: Post-Retrieval Process
4 Lessons
4 Lessons
Enhance RAG response quality with post-retrieval optimization, RAG-fusion, and Cross Encoder reranking.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
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