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Mastering NLP from Foundations to LLMs

You're reading from  Mastering NLP from Foundations to LLMs

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Pages 340 pages
Edition 1st Edition
Languages
Authors (2):
Lior Gazit Lior Gazit
Profile icon Lior Gazit
Meysam Ghaffari Meysam Ghaffari
Profile icon Meysam Ghaffari
View More author details

Table of Contents (14) Chapters

Preface Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts Index Other Books You May Enjoy

Exploring advanced system design – RAG and LangChain

Retrieval-Augmented Generation (RAG) is a development framework designed for seamless interaction with LLMs. LLMs, by virtue of their generalist nature, are capable of performing a vast array of tasks competently. However, their generality often precludes them from delivering detailed, nuanced responses to queries that necessitate specialized knowledge or in-depth expertise in a domain. For instance, if you aspire to use an LLM to address queries concerning a specific discipline, such as law or medicine, it might satisfactorily answer general queries but fail to respond accurately to those needing detailed insights or up-to-date knowledge.

RAG designs offer a comprehensive solution to the limitations typically encountered in LLM processing. In a RAG framework, the text corpus undergoes initial preprocessing, where it’s segmented into summaries or distinct chunks and then embedded within a vector space. When a query...

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