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Advanced RAG Techniques: Choosing the Right Approach

Gain insights into RAG systems by exploring pre- and post-retrieval optimization techniques, and discover how to build intelligent applications using LangChain with practical hands-on experience.

Intermediate

28 Lessons

4h

Certificate of Completion

Gain insights into RAG systems by exploring pre- and post-retrieval optimization techniques, and discover how to build intelligent applications using LangChain with practical hands-on experience.
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This course includes

1 Project
23 Playgrounds
Course Overview
What You'll Learn
Course Content
Recommendations

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...Show More
The course explains RAG, which combines information retrieval and generative models. Learners will gain hands-on experience with...Show More

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
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

1.

Getting Started

2 Lessons

Get familiar with RAG to enhance factual accuracy and build robust NLP applications.

2.

Introduction to Retrieval-Augmented Generation (RAG)

6 Lessons

Look at the integration of retrieval-based and generative models for enhanced NLP tasks.

5.

Advanced RAG: Post-Retrieval Process

4 Lessons

Enhance RAG response quality with post-retrieval optimization, RAG-fusion, and Cross Encoder reranking.

7.

Conclusion

1 Lessons

See how it works integrating RAG techniques for enhanced NLP applications and community engagement.

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