Master Retrieval-Augmented Generation (RAG) Systems: Transform AI Applications with RAG: Expert Techniques in Information Retrieval and Generation

Paulo Dichone

Mex$1640.99

Video Jul 2024 1hr 39mins 1st Edition

Paulo Dichone

Mex$1640.99

Video Jul 2024 1hr 39mins 1st Edition

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Key benefits

In-depth exploration of naive vs. advanced RAG techniques
Techniques for query expansion and document ranking
Advanced methods like cross-encoder re-ranking and dense passage retrieval

Description

In this course, you will embark on a journey to master Retrieval-Augmented Generation (RAG) systems, starting with an introduction to the fundamentals. The initial section sets the stage by explaining the course structure and helping you set up your development environment. As you progress, you will delve into the intricacies of naive RAG, learning about its potential pitfalls and how to avoid them. The course then transitions to advanced RAG techniques, where you will explore methods for expanding generated answers, embedding text chunks, and performing similarity searches. Hands-on exercises will guide you through adding documents to vector stores, generating answers, and projecting embedded results on graphs. These practical sessions ensure you gain a thorough understanding of each concept. As you move forward, you’ll tackle more complex RAG techniques such as query expansion with multiple queries, re-ranking with cross-encoders, and dense passage retrieval (DPR). Each section is designed to build your skills progressively, culminating in a comprehensive understanding of RAG systems. By the end, you will be equipped with the knowledge and skills to implement sophisticated RAG solutions in real-world applications.

Who is this book for?

This course is tailored for AI enthusiasts eager to explore advanced AI and NLP techniques, data scientists seeking to enhance their information retrieval skills, NLP professionals aiming to master RAG systems for complex queries, and tech innovators looking to apply cutting-edge AI in real-world applications. A basic understanding of AI and NLP concepts and proficiency in Python programming are required.