Course Structure

Get an overview of the structure and strengths of the course.

This course aims to give you a deep understanding of RAG and its applications in NLP. We’ll start with the basics and gradually move to more advanced techniques, ensuring a solid and comprehensive learning experience.

Course content structure

The course is divided into five chapters, each focusing on a specific aspect of RAG:

  • Introduction to Retrieval-Augmented Generation (RAG): This chapter dives into the core concepts of RAG. You’ll explore its definition, different approaches (naive, advanced, and modular), and how to choose the right method for your needs. A chapter quiz will solidify your understanding of these foundational concepts.

  • Advanced RAG: Pre-Retrieval (Optimizing Indexing): This chapter dives into advanced techniques for optimizing the pre-retrieval stage of RAG. We’ll explore chunking strategies for text processing, data granularity enhancement, multi-representation indexing, self-querying retrieval (SQR), and parent document retrieval. You’ll solidify your grasp of these techniques through a dedicated chapter quiz.

  • Advanced RAG: Pre-Retrieval (Optimizing Query): We’ll focus on advanced methods for optimizing the pre-retrieval query formulation stage. This chapter explores multi-query strategies, query decomposition techniques, step-back prompting, hypothetical document embeddings (HyDE), semantic routing, and routing with LLM-based classifiers. A chapter quiz will test your knowledge of these advanced query optimization methods.

  • Advanced RAG: Post-Retrieval Process: Here, we explore techniques used in the post-retrieval processing stage of RAG. You’ll learn about reranking methods like RAG-fusion and CrossEncoder reranking, further enhancing the retrieved information for a more precise final output. A chapter quiz will ensure you’ve grasped these post-retrieval techniques.

  • Talk to Your Webpage: A RAG-Powered Chat Interface: To solidify your learning and put your newfound knowledge to practice, you’ll be presented with a hands-on project. This project will involve building a RAG-powered chat interface that can interact with websites, demonstrating the practical applications of RAG technology.

The structure of this course

Course strengths

Below, we’ve outlined the key strengths of this course:

Strength

Description

Structured learning journey

The course follows a logical progression, starting with fundamentals and gradually advancing to sophisticated techniques.

Comprehensive content

The course covers all key aspects of RAG, from core concepts to advanced (pre-retrieval and post-retrieval techniques).

Interactive learning

Step-by-step implementation of each technique and chapter quizzes assess your understanding and reinforce key concepts throughout the course.

Practical application

The hands-on project provides a valuable opportunity to apply the gained knowledge by building a real-world RAG-powered application.

Let’s start our journey to learn RAG!