Advanced RAG Techniques: Choosing the Right Approach

Advanced RAG Techniques: Choosing the Right Approach

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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Project
23 Playgrounds

This course includes

1 Project
23 Playgrounds

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

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

1.

Getting Started

This chapter introduces retrieval-augmented generation (RAG) and provides an overview of the course structure, key strengths, and intended audience.
2.

Introduction to Retrieval-Augmented Generation (RAG)

This chapter explores the fundamentals of RAG, including naive and advanced RAG techniques, modular RAG, and how to choose the right approach for your needs.
3.

Advanced RAG: Pre-Retrieval (Optimizing Indexing)

This chapter introduces key RAG techniques: indexing, chunking, data cleaning, multi-representation indexing, self-querying, and parent document retrieval.
4.

Advanced RAG: Pre-Retrieval (Optimizing Query)

This chapter covers query optimization, multi-query, decomposition, step-back prompting, HyDE, semantic routing, and LLM classifiers to enhance RAG responses.
5.

Advanced RAG: Post-Retrieval Process

This chapter explores post-retrieval optimization, focusing on RAG-Fusion and Cross Encoder reranking to improve response accuracy in Advanced RAG systems.
6.

Conclusion

1 Lesson

In this chapter, you will wrap up your RAG journey, revisit key concepts, and explore potential applications for building powerful NLP tools.

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Evan Dunbar

ML Engineer

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