Overview

This course offers an in-depth exploration of Retrieval-Augmented Generation (RAG) systems, focusing on their practical application in real-world scenarios. By the end of the course, you'll gain expertise in advanced techniques like query expansion, re-ranking, and dense passage retrieval. You'll also understand the core components of RAG systems and learn how to address common challenges in their implementation. The course begins with an introduction to the basic concepts of RAG, providing an essential foundation for understanding both naive and advanced RAG approaches. You'll dive into the RAG triad and learn about the pitfalls associated with early-stage implementations of RAG, followed by an exploration of more sophisticated techniques. The practical sections will guide you step-by-step through hands-on exercises that involve splitting text, embedding chunks, and performing similarity searches. Advanced topics such as query expansion with generated answers, re-ranking using cross-encoders, and the Dense Passage Retrieval (DPR) technique will be explored thoroughly. You’ll also learn to visualize your results through graph projections and plot embeddings for better interpretation of your data. Throughout the course, you’ll get plenty of chances to apply your learning in hands-on sessions and practical challenges. This course is designed for learners with a foundational understanding of machine learning and natural language processing. It's suitable for professionals and developers looking to master advanced RAG systems for more efficient document retrieval and answer generation. Prior knowledge of Python and machine learning frameworks is recommended.

Syllabus

Introduction

In this module, we will introduce the course, explain the significance of RAG systems, and provide an overview of the structure, ensuring you are prepared for the hands-on sessions by setting up the required development environment.

RAG (Retrieval-Augmented Generation) Deep Dive - Naive RAG vs Advanced RAG

In this module, we will take a deeper look at RAG systems, exploring the fundamental concepts of RAG and the RAG triad. We'll also examine the limitations of Naive RAG and provide a comprehensive understanding of its common pitfalls.

Advanced RAG Deep Dive - Advanced Techniques

In this module, we will explore advanced RAG techniques, covering topics such as query expansion, embedding, similarity searches, and answer generation. Practical exercises will guide you through these advanced methods to enhance your understanding.

Hands-on: Advanced RAG Technique - Query Expansion with Multiple Queries

In this module, we will dive into the practical aspects of query expansion with multiple queries. You’ll gain hands-on experience in enhancing retrieval processes through query generation and face a challenge to apply what you've learned.

Hands-on - Advanced RAG Technique: Re-Ranking with Cross-encoder

In this module, we will cover re-ranking techniques, specifically using cross-encoders, and demonstrate how to rank results and pass them through an LLM for relevant answers. We'll also cover practical applications that will solidify your understanding.

Hands-on - Advanced RAG Technique: Dense Passage Retrieval (DPR)

In this module, we will introduce and practice the Dense Passage Retrieval (DPR) technique, providing you with a hands-on session to understand its implementation and application in RAG systems.

Other Advanced RAG Techniques

In this module, we will briefly cover other advanced techniques used in RAG systems, expanding your knowledge and showcasing alternative methods for improving retrieval and generation tasks.

Wrap up - What's Next

In this final module, we will conclude the course by discussing future directions in RAG systems, helping you understand the advancements in this technology and what comes next.