A Simple Guide To Retrieval Augmented Generation
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A Simple Guide To Retrieval Augmented Generation
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Author : Abhinav Kimothi
language : en
Publisher: Simon and Schuster
Release Date : 2025-07-15
A Simple Guide To Retrieval Augmented Generation written by Abhinav Kimothi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-15 with Computers categories.
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.
A Simple Guide To Retrieval Augmented Generation
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Author : Abhinav Kimothi
language : en
Publisher: Simon and Schuster
Release Date : 2025-07-01
A Simple Guide To Retrieval Augmented Generation written by Abhinav Kimothi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Computers categories.
Everything you need to know about Retrieval Augmented Generation in one human-friendly guide. 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 gives an easy, yet comprehensive, introduction to RAG for AI beginners. You’ll go from basic RAG that uses indexing and generation pipelines, to modular RAG and multimodal data from images, spreadsheets, and more. About the Technology If you want to use a large language model to answer questions about your specific business, you’re out of luck. The LLM probably knows nothing about it and may even make up a response. Retrieval Augmented Generation is an approach that solves this class of problems. The model first retrieves the most relevant pieces of information from your knowledge stores (search index, vector database, or a set of documents) and then generates its answer using the user’s prompt and the retrieved material as context. This avoids hallucination and lets you decide what it says. About the Book A Simple Guide to Retrieval Augmented Generation is a plain-English guide to RAG. The book is easy to follow and packed with realistic Python code examples. It takes you concept-by-concept from your first steps with RAG to advanced approaches, exploring how tools like LangChain and Python libraries make RAG easy. And to make sure you really understand how RAG works, you’ll build a complete system yourself—even if you’re new to AI! What’s Inside • RAG components and applications • Evaluating RAG systems • Tools and frameworks for implementing RAG About the Readers For data scientists, engineers, and technology managers—no prior LLM experience required. Examples use simple, well-annotated Python code. About the Author Abhinav Kimothi is a seasoned data and AI professional. He has spent over 15 years in consulting and leadership roles in data science, machine learning and AI, and currently works as a Director of Data Science at Sigmoid. Table of Contents Part 1 1 LLMs and the need for RAG 2 RAG systems and their design Part 2 3 Indexing pipeline: Creating a knowledge base for RAG 4 Generation pipeline: Generating contextual LLM responses 5 RAG evaluation: Accuracy, relevance, and faithfulness Part 3 6 Progression of RAG systems: Naïve, advanced, and modular RAG 7 Evolving RAGOps stack Part 4 8 Graph, multimodal, agentic, and other RAG variants 9 RAG development framework and further exploration
Human Computer Interaction Design And Research
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Author : Nimmi Rangaswamy
language : en
Publisher: Springer Nature
Release Date : 2025-02-13
Human Computer Interaction Design And Research written by Nimmi Rangaswamy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-13 with Computers categories.
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
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Author : Emmanuel Maggiori
language : en
Publisher: Simon and Schuster
Release Date : 2025-07-22
The Ai Pocketbook written by Emmanuel Maggiori and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Computers categories.
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.
Liquid Legal Sustaining The Rule Of Law
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Author : Kai Jacob
language : en
Publisher: Springer Nature
Release Date : 2025-05-05
Liquid Legal Sustaining The Rule Of Law written by Kai Jacob and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-05 with Law categories.
This book is a comprehensive guide for legal, business, and technology professionals seeking to understand the intersection of sustainability and emerging technologies in the legal ecosystem. The book takes a critical look at the role of AI and cloud technologies in promoting sustainable legal practices and addresses the potential risks and ethical considerations associated with these technologies. The book explores the concept of sustainability in a legal context, highlighting the need for the legal system to sustain itself in order to sustain society at large. The chapters cover a wide range of topics, from the use of generative AI and open legal data to promote access to justice, to the codification of corporate cultural intelligence to mitigate risks associated with AI. The book also addresses the potential dark side of AI in the legal market, including the risks of autonomy, liability, legal, and ethical issues that arise when using AI in legal decision-making processes. The authors explore the need for sustainable digital transformation as a prerequisite for sustainable law, highlighting the importance of understanding the ethical and legal implications of AI in the legal system.
Quick Start Guide To Large Language Models
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Author : Sinan Ozdemir
language : en
Publisher: Addison-Wesley Professional
Release Date : 2024-09-26
Quick Start Guide To Large Language Models written by Sinan Ozdemir and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-26 with Computers categories.
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Ultimate Aws Certified Ai Practitioner Aif C01 Exam Guide
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Author : Gaurav H Kankaria
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-07-09
Ultimate Aws Certified Ai Practitioner Aif C01 Exam Guide written by Gaurav H Kankaria and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-09 with Computers categories.
TAGLINE Your Complete Roadmap to AWS AI Practitioner Success—Simplified, Practical, and Designed to Help You Pass with Confidence. KEY FEATURES ● Gain in-depth knowledge of AWS AI services, Generative AI, and ethical considerations for business and technical use cases. ● Master essential AWS AI/ML tools to stay ahead in the evolving landscape of cloud-based artificial intelligence solutions. ● Prepare confidently with real-world examples, clear explanations, and targeted exam questions for the AWS AI Practitioner certification. DESCRIPTION In today’s AI-powered world, earning the AWS Certified AI Practitioner (AIF-C01) certification is a powerful way to validate your skills, boost your credibility, and stand out in the competitive cloud job market. Ultimate AWS Certified AI Practitioner (AIF-C01) Exam Guide is a comprehensive, beginner-friendly roadmap for professionals, students, and decision-makers looking to master AI and Machine Learning on AWS—and crack the AIF-C01 exam with confidence. Covering everything from AI and ML fundamentals to core AWS services like SageMaker, Bedrock, and Rekognition, this guide also explores Generative AI, vision and language-based AI use cases, and practical tools for personalization, security, and governance. You'll gain clarity on responsible AI principles, learn to identify and mitigate bias, and confidently navigate AWS best practices in ethics and compliance. Each chapter offers real-world examples, exam strategies, and practice questions designed to reinforce key concepts and simulate the exam environment. Whether you're technical or non-technical, the content is simplified for easy understanding—without sacrificing depth or relevance. If you're serious about working in AI or cloud, this certification isn't just a bonus—it’s becoming a must-have. Don’t miss your chance to stay ahead of the curve—master AWS AI and future-proof your career now. WHAT WILL YOU LEARN ● Understand foundational concepts of AI, Machine Learning, and Generative AI for modern cloud applications. ● Gain hands-on experience with AWS AI/ML services like SageMaker, Bedrock and Rekognition to build intelligent solutions. ● Learn to build, train, fine-tune, and deploy machine learning models using Amazon SageMaker. ● Apply responsible AI practices by identifying and mitigating ethical risks, biases, and fairness issues in AI solutions. ● Secure your AI workloads through AWS best practices in governance, compliance, and data protection. ● Access targeted exam tips, mock questions, and real-world examples to confidently clear the AWS AI Practitioner certification. WHO IS THIS BOOK FOR? This book is ideal for aspiring AI/cloud professionals, tech sales teams, business leaders, and students seeking a foundational understanding of artificial intelligence using AWS. Whether you're new to cloud or aiming to crack the AWS Certified AI Practitioner (AIF-C01) exam, this guide equips you with the essential skills to succeed. TABLE OF CONTENTS 1. Introduction to the AWS AI Practitioner Certification Exam 2. Overview of AI and ML on AWS 3. Core AWS Services and Tools for AI and ML 4. Introduction to Gen AI and AWS Gen AI Services 5. Key Use Cases of Generative AI on AWS 6. Building AI Solutions with Amazon SageMaker 7. Other AWS AI Services 8. Ethics, Bias, and Responsible AI Practices 9. Security and Governance Best Practices for AI 10. Exam Tips, Practice Questions, and the Future of AI Index
Generative Ai For Software Engineers The Journey Begins
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Author : Naresh Dulam
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2023-07-23
Generative Ai For Software Engineers The Journey Begins written by Naresh Dulam and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-23 with Computers categories.
This book assists software engineers as they explore the realm of Artificial Intelligence (AI), providing essential tools and techniques for creating new AI-powered applications or adding AI functionalities to their current projects. Significant advancements in AI and machine learning (ML) frameworks have allowed software engineers to easily integrate intelligent capabilities into their products or projects in the last ten years. The introduction of ChatGPT in late 2022 brought Generative AI into the spotlight. Although abundant information is available online, software engineers need help finding a clear entry point like 101 to learn fundamentals. This guide simplifies the core concepts, gradually addressing more advanced topics to enable you to create practical, production-ready AI solutions with concrete code examples. Note: This book simplifies complex concepts by applying abstractions for software engineers to grasp the basics of Generative AI.
Mastering Spring Ai
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Author : Banu Parasuraman
language : en
Publisher: Springer Nature
Release Date : 2024-12-01
Mastering Spring Ai written by Banu Parasuraman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-01 with Computers categories.
Dive into the future of programming with this comprehensive guide for Java developers to integrate large language models (LLMs) and Generative AI using the Spring Framework. This book comes at a revolutionary time when AI technology is transforming how we implement solutions in various fields, including natural language processing, content generation, and predictive analytics. With its widespread use in the Java community, the Spring Framework is a logical choice for this integration. By focusing on integrating LLMs and GenAI with Spring, this book bridges a significant gap between cutting-edge AI technologies and traditional Java development practices. The author uses a hands-on approach, guiding you through practical implementation to effectively show how to apply theory in real-world situations. Basic introductions of topics—Spring AI, Spring Framework, and other related AI technologies—evolve into advanced integrations to ensure that you find valuable insights regardless of your starting level. Additionally, this book dedicates sections to security and ethical considerations, addressing the pressing issues associated with AI. With a look at emerging trends and future developments, this book prepares you for what's next, ensuring that you are not just catching up with the current state of technology but are also ready for future advancements. What You Will Learn • Master the integration of LLMs and GenAI with the Spring Framework • Develop practical skills in developing AI-driven applications using Java • Gain insights into handling data, security, and ethical considerations in AI applications • Apply strategies for optimizing performance and scalability in AI-enabled applications • Prepare for future AI trends and technologies Who This Book Is For Intermediate to advanced Java developers who are familiar with the Spring Framework, including concepts such as dependency injection, Spring Boot, and building RESTful services. This foundational knowledge will help developers grasp the more advanced topics of integrating AI technologies with Spring. Prior knowledge of basic AI concepts and machine learning is helpful but not essential as the book covers these topics from the ground up.
Mastering Retrieval Augmented Generation
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Author : Prashanth Josyula
language : en
Publisher: BPB Publications
Release Date : 2025-03-21
Mastering Retrieval Augmented Generation written by Prashanth Josyula and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-21 with Computers categories.
DESCRIPTION Large language models (LLMs) like GPT, BERT, and T5 are revolutionizing how we interact with technology — powering virtual assistants, content generation, and data analysis. As their influence grows, understanding their architecture, capabilities, and ethical considerations is more important than ever. This book breaks down the essentials of LLMs and explores retrieval-augmented generation (RAG), a powerful approach that combines retrieval systems with generative AI for smarter, faster, and more reliable results. It provides a step-by-step approach to building advanced intelligent systems that utilize an innovative technique known as the RAG thus making them factually correct, context-aware, and sustainable. You will start with foundational knowledge — understanding architectures, training processes, and ethical considerations — before diving into the mechanics of RAG, learning how retrievers and generators collaborate to improve performance. The book introduces essential frameworks like LangChain and LlamaIndex, walking you through practical implementations, troubleshooting, and optimization techniques. It explores advanced optimization techniques, and offers hands-on coding exercises to ensure practical understanding. Real-world case studies and industry applications help bridge the gap between theory and implementation. By the final chapter, you will have the skills to design, build, and optimize RAG-powered applications — integrating LLMs with retrieval systems, creating custom pipelines, and scaling for performance. Whether you are an experienced AI professional or an aspiring developer, this book equips you with the knowledge and tools to stay ahead in the ever-evolving world of AI. WHAT YOU WILL LEARN ● Understand the fundamentals of LLMs. ● Explore RAG and its key components. ● Build GenAI applications using LangChain and LlamaIndex frameworks. ● Optimize retrieval strategies for accurate and grounded AI responses. ● Deploy scalable, production-ready RAG pipelines with best practices. ● Troubleshoot and fine-tune RAG pipelines for optimal performance. WHO THIS BOOK IS FOR This book is for AI practitioners, data scientists, students, and developers looking to implement RAG using LangChain and LlamaIndex. Readers having basic knowledge of Python, ML concepts, and NLP fundamentals would be able to leverage the knowledge gained to accelerate their careers. TABLE OF CONTENTS 1. Introduction to Large Language Models 2. Introduction to Retrieval-augmented Generation 3. Getting Started with LangChain 4. Fundamentals of Retrieval-augmented Generation 5. Integrating RAG with LangChain 6. Comprehensive Guide to LangChain 7. Introduction to LlamaIndex 8. Building and Optimizing RAG Pipelines with LlamaIndex 9. Advanced Techniques with LlamaIndex 10. Deploying RAG Models in Production 11. Future Trends and Innovations in RAG