The Embedding Meaning
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Unlocking Data with Generative AI and RAG
Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantage Key Features Optimize data retrieval and generation using vector databases Boost decision-making and automate workflows with AI agents Overcome common challenges in implementing real-world RAG systems Purchase of the print or Kindle book includes a free PDF eBook Book Description Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes. The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies. By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique. What you will learn Understand RAG principles and their significance in generative AI Integrate LLMs with internal data for enhanced operations Master vectorization, vector databases, and vector search techniques Develop skills in prompt engineering specific to RAG and design for precise AI responses Familiarize yourself with AI agents' roles in facilitating sophisticated RAG applications Overcome scalability, data quality, and integration issues Discover strategies for optimizing data retrieval and AI interpretability Who this book is for This book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.
From fieldwork to linguistic theory
Author: Edward Gibson
language: en
Publisher: Language Science Press
Release Date: 2024-09-25
Dan Everett is a renowned linguist with an unparalleled breadth of contributions, ranging from fieldwork to linguistic theory, including phonology, morphology, syntax, semantics, sociolinguistics, psycholinguistics, historical linguistics, philosophy of language, and philosophy of linguistics. Born on the U.S. Mexican border, Daniel Everett faced much adversity growing up and was sent as a missionary to convert the Pirahã in the Amazonian jungle, a group of people who speak a language that no outsider had been able to become proficient in. Although no Pirahã person was successfully converted, Everett successfully learned and studied Pirahã, as well as multiple other languages in the Americas. Ever steadfast in pursuing data-driven language science, Everett debunked generativist claims about syntactic recursion, for which he was repeatedly attacked. In addition to conducting fieldwork with many understudied languages and revolutionizing linguistics, Everett has published multiple works for the general public: "Don’t sleep, there are snakes, Language: The cultural tool, and how language began". This book is a collection of 15 articles that are related to Everett’s work over the years, released after a tribute event for Dan Everett that was held at MIT on June 8th 2023.
Text as Data
Author: Justin Grimmer
language: en
Publisher: Princeton University Press
Release Date: 2022-01-04
A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry