The course explains advanced RAG concepts, showcasing their implementation through LangChain for enhanced practical learning and application.
Intermediate
28 Lessons
4h
Certificate of Completion
The course explains advanced RAG concepts, showcasing their implementation through LangChain for enhanced practical learning and application.
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This course includes
This course includes
Course Overview
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 po...
TAKEAWAY SKILLS
Generative Ai
Natural Language Processing
What You'll Learn
An understanding of the core principles of retrieval-augmented generation (RAG) and how it improves the accuracy of large language models
Familiarity with different RAG implementation approaches and how to choose the most suitable approach for specific applications
Hands-on experience implementing RAG models using LangChain
The ability to use advanced pre-retrieval optimization techniques for indexing and query optimization
Hands-on experience in post-retrieval processing methods, like RAG-fusion, to refine retrieved information for more accurate results
The ability to design and develop RAG-based chatbots
What You'll Learn
An understanding of the core principles of retrieval-augmented generation (RAG) and how it improves the accuracy of large language models
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Course Content
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
Conclusion
1 Lesson
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