Rag In Action Building The Future Of Ai Driven Applications
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Rag In Action Building The Future Of Ai Driven Applications
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Author : Srinivasan Ramalingam
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2023-12-08
Rag In Action Building The Future Of Ai Driven Applications written by Srinivasan Ramalingam 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 2023-12-08 with Computers categories.
Retrieval-Augmented Generation, or RAG, represents a transformative approach in the field of artificial intelligence, merging the capabilities of retrieval systems with generative models to create more informed and contextual responses. At its core, RAG seeks to enhance the output of large language models (LLMs) by supplementing their generative capacities with relevant external data from retrieval systems. This hybrid approach addresses one of the primary limitations of standalone generative models—namely, their reliance on pre-trained knowledge that might not reflect the most current or context-specific information. The architecture of RAG is meticulously designed to dynamically query a database or a search engine, thus enabling the model to ground its responses in the latest or user-specific data, which is particularly crucial for applications requiring up-to-date and precise information, such as financial analytics or personalized healthcare advice. Through the synergistic combination of retrieval and generation, RAG enables systems to bridge the gap between static knowledge embedded within LLMs and the vast, ever-evolving sea of external data, pushing the envelope of what AI-driven applications can achieve (Lewis et al., 2020; Karpukhin et al., 2020; Guu et al., 2020).
Building Data Driven Applications With Llamaindex
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Author : Andrei Gheorghiu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-10
Building Data Driven Applications With Llamaindex written by Andrei Gheorghiu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-10 with Computers categories.
Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications Key Features Examine text chunking effects on RAG workflows and understand security in RAG app development Discover chatbots and agents and learn how to build complex conversation engines Build as you learn by applying the knowledge you gain to a hands-on project Book DescriptionDiscover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.What you will learn Understand the LlamaIndex ecosystem and common use cases Master techniques to ingest and parse data from various sources into LlamaIndex Discover how to create optimized indexes tailored to your use cases Understand how to query LlamaIndex effectively and interpret responses Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit Customize a LlamaIndex configuration based on your project needs Predict costs and deal with potential privacy issues Deploy LlamaIndex applications that others can use Who this book is for This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.
Building Ai Agents With Llms Rag And Knowledge Graphs
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Author : Salvatore Raieli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-11
Building Ai Agents With Llms Rag And Knowledge Graphs written by Salvatore Raieli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.What you will learn Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data Build and query knowledge graphs for structured context and factual grounding Develop AI agents that plan, reason, and use tools to complete tasks Integrate LLMs with external APIs and databases to incorporate live data Apply techniques to minimize hallucinations and ensure accurate outputs Orchestrate multiple agents to solve complex, multi-step problems Optimize prompts, memory, and context handling for long-running tasks Deploy and monitor AI agents in production environments Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
From On Premises To Cloud Using Gen Ai The Future Of Software Development
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Author : Gangadhararamachary Ramadugu
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2025-02-08
From On Premises To Cloud Using Gen Ai The Future Of Software Development written by Gangadhararamachary Ramadugu and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-08 with Computers categories.
Prompt engineering is a new and exciting skill focused on how to better understand and apply generative models to your tasks and use cases. Effective prompt engineering helps you push the boundaries of generative AI and get the most out of your generative-based applications. The text that you send into a generative model is typically called the “prompt”. This prompt is passed to the model during inference time to generate a “completion”. Below is an example question-answer prompt and completion between a “Human” and the generative AI “Assistant”. Note that the generative model is simply completing the Human’s prompt following the term “Assistant:”
Ultimate Snowflake Cortex Ai For Generative Ai Applications Design Build And Deploy Generative Ai Solutions With Snowflake Cortex For Real World And Industry Scale Applications
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Author : Krishnan Srinivasan
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-06-21
Ultimate Snowflake Cortex Ai For Generative Ai Applications Design Build And Deploy Generative Ai Solutions With Snowflake Cortex For Real World And Industry Scale Applications written by Krishnan Srinivasan and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-21 with Computers categories.
Power your AI Journey and Build the Future with Snowflake Cortex. Key Features● Build enterprise-ready GenAI apps using Snowflake Cortex tools and APIs.● Implement RAG, AI Agents, and Document AI with real-world precision.● Explore practical Cortex use cases across industries and domains. Book DescriptionSnowflake Cortex is redefining how modern enterprises build, scale, and deploy Generative AI—natively within the data cloud. Ultimate Snowflake Cortex AI for Generative AI Applications is a hands-on, end-to-end guide designed for data professionals, engineers, and technical leaders eager to unlock the full power of Snowflake’s native AI engine. The book begins by grounding you in the fundamentals of AI/ML within the Snowflake ecosystem before diving deep into the architecture, capabilities, and use cases of Snowflake Cortex. As you progress, you’ll explore Cortex’s built-in machine learning functions, dive into prompt engineering, Retrieval-Augmented Generation (RAG), and learn how to leverage LLM functions effectively. You'll gain hands-on experience in fine-tuning models, translating natural language queries into actionable insights, and automating document processing using Cortex’s Document AI. Practical chapters on security, governance, and cost discipline ensure you're prepared for enterprise-scale AI deployment. With real-world case studies and cross-industry applications, this book equips you with both the strategic understanding and technical skills to implement Generative AI at scale. Cortex is the future of enterprise AI—don’t just adapt to it, lead it. What you will learn● Build and deploy Generative AI apps using Snowflake Cortex.● Understand and apply Cortex's built-in LLM functions effectively.● Fine-tune LLMs for domain-specific, enterprise-grade applications.● Use RAG and prompt engineering for accurate AI responses.● Extract insights from structured and unstructured enterprise data.● Automate document workflows using Cortex’s Document AI features.● Solve cross-industry problems with real-world Cortex implementations.
Generative Ai And Implications For Ethics Security And Data Management
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Author : Gomathi Sankar, Jeganathan
language : en
Publisher: IGI Global
Release Date : 2024-08-21
Generative Ai And Implications For Ethics Security And Data Management written by Gomathi Sankar, Jeganathan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-21 with Computers categories.
As generative AI rapidly advances with the field of artificial intelligence, its presence poses significant ethical, security, and data management challenges. While this technology encourages innovation across various industries, ethical concerns regarding the potential misuse of AI-generated content for misinformation or manipulation may arise. The risks of AI-generated deepfakes and cyberattacks demand more research into effective security tactics. The supervision of datasets required to train generative AI models raises questions about privacy, consent, and responsible data management. As generative AI evolves, further research into the complex issues regarding its potential is required to safeguard ethical values and security of people’s data. Generative AI and Implications for Ethics, Security, and Data Management explores the implications of generative AI across various industries who may use the tool for improved organizational development. The security and data management benefits of generative AI are outlined, while examining the topic within the lens of ethical and social impacts. This book covers topics such as cybersecurity, digital technology, and cloud storage, and is a useful resource for computer engineers, IT professionals, technicians, sociologists, healthcare workers, researchers, scientists, and academicians.
Generative Ai In Action
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Author : Amit Bahree
language : en
Publisher: Simon and Schuster
Release Date : 2024-11-26
Generative Ai In Action written by Amit Bahree 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 2024-11-26 with Computers categories.
Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI in Action is the comprehensive and concrete guide to generative AI you’ve been searching for. It introduces both AI’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: • A practical overview of of generative AI applications • Architectural patterns, integration guidance, and best practices for generative AI • The latest techniques like RAG, prompt engineering, and multi-modality • The challenges and risks of generative AI like hallucinations and jailbreaks • How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You’ll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside • Best practices for deploying Generative AI apps • Production-quality RAG • Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1 1 Introduction to generative AI 2 Introduction to large language models 3 Working through an API: Generating text 4 From pixels to pictures: Generating images 5 What else can AI generate? Part 2 6 Guide to prompt engineering 7 Retrieval-augmented generation: The secret weapon 8 Chatting with your data 9 Tailoring models with model adaptation and fine-tuning Part 3 10 Application architecture for generative AI apps 11 Scaling up: Best practices for production deployment 12 Evaluations and benchmarks 13 Guide to ethical GenAI: Principles, practices, and pitfalls A The book’s GitHub repository B Responsible AI tools
Machine Learning And Generative Ai For Marketing
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Author : Yoon Hyup Hwang
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-30
Machine Learning And Generative Ai For Marketing written by Yoon Hyup Hwang and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
Start transforming your data-driven marketing strategies and increasing customer engagement. Learn how to create compelling marketing content using advanced gen AI techniques and stay in touch with the future AI ML landscape. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Enhance customer engagement and personalization through predictive analytics and advanced segmentation techniques Combine Python programming with the latest advancements in generative AI to create marketing content and address real-world marketing challenges Understand cutting-edge AI concepts and their responsible use in marketing Book Description In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience. This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales. Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards By the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge. What you will learn Master key marketing KPIs with advanced computational techniques Use explanatory data analysis to drive marketing decisions Leverage ML models to predict customer behaviors, engagement levels, and customer lifetime value Enhance customer segmentation with ML and develop highly personalized marketing campaigns Design and execute effective A/B tests to optimize your marketing decisions Apply natural language processing (NLP) to analyze customer feedback and sentiments Integrate ethical AI practices to maintain privacy in data-driven marketing strategies Who this book is for This book targets a diverse group of professionals: Data scientists and analysts in the marketing domain looking to apply advanced AI ML techniques to solve real-world marketing challenges Machine learning engineers and software developers aiming to build or integrate AI-driven tools and applications for marketing purposes Marketing professionals, business leaders, and entrepreneurs who must understand the impact of AI on marketing Reader are presumed to have a foundational proficiency in Python and a basic to intermediate grasp of ML principles and data science methodologies.
Kubernetes For Generative Ai Solutions
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Author : Ashok Srirama
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-06-06
Kubernetes For Generative Ai Solutions written by Ashok Srirama and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.
Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples. Key Features Build and deploy your first Generative AI workload on Kubernetes with confidence Learn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automation Gain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloads Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management. This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience. By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.What you will learn Explore GenAI deployment stack, agents, RAG, and model fine-tuning Implement HPA, VPA, and Karpenter for efficient autoscaling Optimize GPU usage with fractional allocation, MIG, and MPS setups Reduce cloud costs and monitor spending with Kubecost tools Secure GenAI workloads with RBAC, encryption, and service meshes Monitor system health and performance using Prometheus and Grafana Ensure high availability and disaster recovery for GenAI systems Automate GenAI pipelines for continuous integration and delivery Who this book is for This book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.
Building Llms For Production
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Author : Louis-François Bouchard
language : en
Publisher: Towards AI, Inc.
Release Date : 2024-05-21
Building Llms For Production written by Louis-François Bouchard and has been published by Towards AI, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-21 with Computers categories.
“This is the most comprehensive textbook to date on building LLM applications - all essential topics in an AI Engineer's toolkit." - Jerry Liu, Co-founder and CEO of LlamaIndex (THE BOOK WAS UPDATED ON OCTOBER 2024) With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future. The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python. What's Inside this 470-page Book (Updated October 2024)? - Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning - Roadmap for Building Production-Ready Applications using LLMs - Fundamentals of LLM Theory - Simple-to-Advanced LLM Techniques & Frameworks - Code Projects with Real-World Applications - Colab Notebooks that you can run right away Community access and our own AI Tutor Table of Contents - Chapter I Introduction to Large Language Models - Chapter II LLM Architectures & Landscape - Chapter III LLMs in Practice - Chapter IV Introduction to Prompting - Chapter V Retrieval-Augmented Generation - Chapter VI Introduction to LangChain & LlamaIndex - Chapter VII Prompting with LangChain - Chapter VIII Indexes, Retrievers, and Data Preparation - Chapter IX Advanced RAG - Chapter X Agents - Chapter XI Fine-Tuning - Chapter XII Deployment and Optimization Whether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way.