Retrieval-Augmented Generation
May 1, 2024
3 minute read
What is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) is a cutting-edge AI technique that revolutionizes natural language processing and code generation. It combines the strengths of retrieval-based methods with the power of generative models, enabling machines to produce more informative, coherent, and engaging text and code.
How Does RAG Work?
RAG leverages a two-step process to generate text or code. Firstly, it retrieves relevant information from a massive knowledge base, such as the vast expanse of the internet. This retrieved information provides contextual knowledge and relevant facts to guide the generation process.
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Find a path to becoming a Retrieval-Augmented Generation. Learn more at:
OpenCourser.com/topic/6441o6/retrieval
Reading list
We've selected eight 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.
Delves into the fundamentals of generative models, which are essential for understanding the inner workings of retrieval-augmented generation. It provides a solid foundation in the underlying principles and techniques used in RAG.
This guide provides a comprehensive overview of natural language generation, covering both the theoretical foundations and practical techniques used in the field. It valuable resource for understanding the broader context of RAG and its applications in natural language processing.
Provides a comprehensive overview of deep learning techniques for natural language processing, which are essential for understanding the inner workings of retrieval-augmented generation. It covers a wide range of topics, from word embeddings to transformer networks.
While not exclusively focused on RAG, this book provides a solid foundation in transformer-based natural language processing models, which are commonly used in RAG.
This classic text on information retrieval provides a comprehensive foundation in the retrieval-based methods that underpin RAG. It covers a wide range of topics, from text representation and indexing to query processing and evaluation.
Explores the use of RAG in educational applications such as personalized learning, adaptive assessment, and language learning. It provides insights into how RAG can improve teaching and learning outcomes.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/6441o6/retrieval
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