Course Structure
Get an overview of the structure and strengths of the course.
We'll cover the following
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.
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!