A Simple Guide To Retrieval Augmented Generation
Download A Simple Guide To Retrieval Augmented Generation PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get A Simple Guide To Retrieval Augmented Generation book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
A Simple Guide to Retrieval Augmented Generation
Author: Abhinav Kimothi
language: en
Publisher: Simon and Schuster
Release Date: 2025-07-15
Everything you need to know about Retrieval Augmented Generation in one human-friendly guide. Generative AI models struggle when you ask them about facts not covered in their training data. Retrieval Augmented Generation—or RAG—enhances an LLM’s available data by adding context from an external knowledge base, so it can answer accurately about proprietary content, recent information, and even live conversations. RAG is powerful, and with A Simple Guide to Retrieval Augmented Generation, it’s also easy to understand and implement! In A Simple Guide to Retrieval Augmented Generation you’ll learn: • The components of a RAG system • How to create a RAG knowledge base • The indexing and generation pipeline • Evaluating a RAG system • Advanced RAG strategies • RAG tools, technologies, and frameworks A Simple Guide to Retrieval Augmented Generation shows you how to enhance an LLM with relevant data, increasing factual accuracy and reducing hallucination. Your customer service chatbots can quote your company’s policies, your teaching tools can draw directly from your syllabus, and your work assistants can access your organization’s minutes, notes, and files. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book A Simple Guide to Retrieval Augmented Generation makes RAG simple and easy, even if you’ve never worked with LLMs before. This book goes deeper than any blog or YouTube tutorial, covering fundamental RAG concepts that are essential for building LLM-based applications. You’ll be introduced to the idea of RAG and be guided from the basics on to advanced and modularized RAG approaches—plus hands-on code snippets leveraging LangChain, OpenAI, Transformers, and other Python libraries. Chapter-by-chapter, you’ll build a complete RAG enabled system and evaluate its effectiveness. You’ll compare and combine accuracy-improving approaches for different components of RAG, and see what the future holds for RAG. You’ll also get a sense of the different tools and technologies available to implement RAG. By the time you’re done reading, you’ll be ready to start building RAG enabled systems. About the reader For data scientists, machine learning and software engineers, and technology managers who wish to build LLM-based applications. Examples in Python—no experience with LLMs necessary. About the author Abhinav Kimothi is an entrepreneur and Vice President of Artificial Intelligence at Yarnit. He has spent over 15 years consulting and leadership roles in data science, machine learning and AI.
Human-Computer Interaction. Design and Research
This two-volume proceedings, set CCIS 2337-2338, constitutes of the proceedings of 15th Indian Conference on Human-Computer Interaction Design and Research, IndiaHCI 2024, held in Mumbai, India, during November 7–9, 2024. The 30 full papers and 12 short papers included in this volume were carefully reviewed and selected from 235 submissions. These papers belong to various tracks which have been divided between the two volumes as follows: - Part I: Paper Track. Part II: Game Design Track, Student Research Consortium Track; Posters and demos Track; Artworks and installations Track.
The AI Pocketbook
Author: Emmanuel Maggiori
language: en
Publisher: Simon and Schuster
Release Date: 2025-07-22
Everything you need to know about AI to survive--and thrive--as an engineer. Haven't you heard? AI can instantly generate code, find and track network intrusions, parse your observability data, and even write a Medium article about it all. If AI is making you sweat about your future as an engineer, don't worry. Your job has never been safer! The AI Pocket Book tells you everything you need to surf the AI wave instead of drowning in it. In The AI Pocket Book you'll get: * Deciphering AI jargon (there's lots of it!) * Where AI fits within your field of engineering * Why AI hallucinates--and what to do about it * What to do when AI comes for your job * Balancing skepticism with unrealistic expectations The AI Pocket Book gives you Emmanuel Maggiori's unvarnished and opinionated take on where AI can be useful, and where it still kind of sucks. Whatever your tech field, this short-and-sweet guide delivers the facts and techniques you'll need in the workplace of the present. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book The AI Pocket Book crams everything engineers need to know about AI into one short volume you can fit into your pocket. You'll take a peek inside the AI black box for an overview of transformers, LLMs, hallucinations, tokens, and embeddings, along with the modern ecosystem of AI models and tools. You'll find out when putting AI first fails your customers, understand how to get from "almost good enough" to "excellent," and pick up some tips for dealing with the inevitable, potentially expensive, screw ups. About the reader For engineers in all fields, from software to security. About the author Emmanuel Maggiori, PhD, is a 10-year AI industry insider who specializes in machine learning and scientific computing. He has developed AI for everything from processing satellite images to packaging deals for holiday travelers. Emmanuel Maggiori is the author of Smart Until It's Dumb and Siliconned.