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Master Retrieval-Augmented Generation (RAG) Systems
Master Retrieval-Augmented Generation (RAG) Systems

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

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Profile Icon Paulo Dichone
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€18.99 per month
Video Jul 2024 1hr 39mins 1st Edition
Video
€59.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon Paulo Dichone
Arrow right icon
€18.99 per month
Video Jul 2024 1hr 39mins 1st Edition
Video
€59.99
Subscription
Free Trial
Renews at €18.99p/m
Video
€59.99
Subscription
Free Trial
Renews at €18.99p/m

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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.

What you will learn

  • Implement naive and advanced RAG techniques
  • Optimize document retrieval and answer generation
  • Apply query expansion with multiple queries
  • Utilize cross-encoder and bi-encoder re-ranking
  • Perform dense passage retrieval effectively
  • Integrate advanced RAG techniques into AI applications

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 24, 2024
Length: 1hr 39mins
Edition : 1st
Language : English
ISBN-13 : 9781836646075
Category :
Languages :

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Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
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Product Details

Publication date : Jul 24, 2024
Length: 1hr 39mins
Edition : 1st
Language : English
ISBN-13 : 9781836646075
Category :
Languages :

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Table of Contents

8 Chapters
Introduction Chevron down icon Chevron up icon
RAG (Retrieval-Augmented Generation) Deep Dive - Naive RAG vs Advanced RAG Chevron down icon Chevron up icon
Advanced RAG Deep Dive - Advanced Techniques Chevron down icon Chevron up icon
Hands-on: Advanced RAG Technique - Query Expansion with Multiple Queries Chevron down icon Chevron up icon
Hands-on - Advanced RAG Technique: Re-Ranking with Cross-encoder Chevron down icon Chevron up icon
Hands-on - Advanced RAG Technique: Dense Passage Retrieval (DPR) Chevron down icon Chevron up icon
Other Advanced RAG Techniques Chevron down icon Chevron up icon
Wrap up - What's Next Chevron down icon Chevron up icon
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