Retrieval-Augmented Generation (RAG)
May 1, 2024
Updated July 18, 2025
16 minute read
Retrieval-Augmented Generation (RAG) is a powerful natural language processing (NLP) technique that combines the strengths of retrieval-based and generative approaches to achieve state-of-the-art results in a variety of language-related tasks such as question answering, text summarization, machine translation, and conversational AI.
How does Retrieval-Augmented Generation (RAG) work?
RAG works by first retrieving a set of relevant documents from a large corpus of text. These documents are then used to train a generative model, which is able to generate new text that is both informative and coherent. The generative model is typically a transformer-based model, such as BERT or GPT-3.
The key advantage of RAG over traditional generative models is that it allows the model to access external knowledge during the generation process. This enables the model to generate text that is more factually accurate and comprehensive. In addition, RAG can be used to generate text in a variety of styles and genres, making it a highly versatile tool for NLP tasks.
Benefits of learning Retrieval-Augmented Generation (RAG)
RAG is a valuable skill for anyone who works with natural language data. With RAG, you are able to:
- Generate high-quality text for a variety of NLP tasks
- Access external knowledge during the generation process
- Generate text in a variety of styles and genres
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Find a path to becoming a Retrieval-Augmented Generation (RAG). Learn more at:
OpenCourser.com/topic/bhzi9v/retrieval
Reading list
We've selected 28 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Retrieval-Augmented Generation (RAG).
LangChain widely used framework for developing applications powered by language models, including RAG systems and AI agents. offers a practical guide to using LangChain and LangGraph, providing hands-on experience with implementing RAG and related concepts.
Provides a comprehensive overview of natural language processing, including a chapter on retrieval-augmented generation. It is written by a leading researcher in the field and is suitable for both beginners and experienced practitioners.
この本は、自然言語処理の理論と実装に関する包括的な概要を提供します。最新の技術とベストプラクティスを網羅しており、この分野の第一人者によって書かれています。
Ce livre fournit un aperçu complet du traitement automatique des langues. Il couvre les dernières techniques et bonnes pratiques, et est écrit par un chercheur de premier plan dans le domaine.
Provides a comprehensive overview of deep learning for natural language processing. It covers the latest techniques and best practices, and is written by a leading researcher in the field.
Transformers are the fundamental architecture behind most modern Large Language Models used in RAG. provides a comprehensive guide to working with transformers using the Hugging Face ecosystem. It offers essential background knowledge for understanding the generative component of RAG systems.
Large Language Models are a core component of RAG. provides a practical, hands-on guide to understanding and working with LLMs, covering transformers, tokenizers, and semantic search. It offers essential background knowledge for anyone building RAG systems and valuable reference for practitioners.
Is specifically designed for beginners interested in Retrieval Augmented Generation (RAG). It aims to introduce the core concepts and guide readers in building basic RAG systems. It serves as a good starting point for those with minimal prior knowledge of RAG.
This project-based book helps solidify understanding of Large Language Models and their applications, including the use of Vector Databases and LangChain, which are highly relevant to RAG implementation. It provides practical experience through building various LLM projects.
For those wanting a deep understanding of the generation component in RAG, this book guides you through building an LLM from scratch. It covers the internal workings, limitations, and customization methods. While challenging, it provides a solid foundation in LLM architecture and training.
Explores vector databases and their role in modern data analytics, including applications in AI and generative AI. Understanding vector databases is fundamental to building efficient RAG systems. This book provides a good overview of this essential technology.
Aimed at beginners, this book provides a comprehensive roadmap to understanding Retrieval Augmented Generation (RAG) technology. It covers core principles, architecture, training processes, and real-world applications. This valuable resource for those new to RAG looking to gain foundational knowledge.
Provides a comprehensive overview of neural network methods in natural language processing, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about the use of neural networks in natural language processing.
Focuses on generative models, which are key to the generation aspect of RAG. It covers various generative techniques and their applications, providing valuable background for understanding how LLMs produce output. The second edition includes recent advancements in the field.
Authored by the creator of Keras, this book provides an accessible introduction to deep learning with Python. The third edition includes updated content on generative AI and large language models, making it highly relevant for understanding the generation aspect of RAG. It helps solidify understanding through practical examples.
Focuses specifically on applying deep learning techniques to natural language processing problems. It delves into advanced concepts and models relevant to the generative component of RAG systems. It is suitable for those looking to deepen their understanding of the neural network architectures used in NLP.
Provides a comprehensive overview of text generation, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about the use of generation in natural language processing.
Provides a comprehensive overview of deep learning for natural language processing, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about RAG and its applications.
Considered a classic in the field, this book provides a broad and deep understanding of Natural Language Processing. While not specifically about RAG, it lays crucial groundwork in areas like text processing, language models (earlier forms), and information extraction, which are prerequisites for understanding RAG. It is commonly used as a textbook in academic institutions.
This classic textbook provides a comprehensive introduction to the field of Information Retrieval, covering fundamental concepts like indexing, querying, and evaluation. Understanding these principles is essential for grasping the 'Retrieval' aspect of RAG systems. It valuable reference and commonly used in academic settings.
This practical book guides you in building NLP applications using Python and deep learning. It covers topics relevant to RAG components, such as text representation and understanding. It useful resource for gaining practical skills in the areas that underpin RAG.
Delves into the application of deep learning techniques to Natural Language Processing and Speech Recognition. It provides a strong technical foundation in the neural network models and algorithms that are fundamental to the generative component of RAG systems and related NLP tasks.
This foundational textbook offers a comprehensive introduction to deep learning, covering theoretical concepts and practical techniques. Deep learning is integral to both the retrieval (e.g., embeddings) and generation (LLMs) components of RAG. While technically challenging, it must-read for a deep understanding of the underlying models.
Provides a comprehensive overview of machine learning for natural language processing, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about RAG and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/bhzi9v/retrieval
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