Generative AI - Text Generation Strategies for LLMs
Generative AI - Text Generation Strategies for LLMs

Author: Anand Vemula

Publisher: Anand Vemula

Published:

Total Pages: 72

ISBN-13:

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This book delves into the fascinating world of generative AI and explores how Large Language Models (LLMs) are revolutionizing text creation. It equips you with a foundational understanding of these AI models and empowers you to leverage their capabilities for various purposes. Part 1: Foundational Concepts sets the stage by introducing generative AI and its application in text generation. It unveils the inner workings of LLMs, explaining how these AI models are specifically designed to understand and process human language. You'll explore the vast potential of LLM-powered text generation, from crafting poems and scripts to generating product descriptions and social media content. Part 2: Applications and Use Cases dives into the practical applications of LLMs. You'll discover how LLMs can be used to generate different creative text formats like poetry, code, and even movie scripts. Explore techniques for style transfer and textual mashups, allowing you to create unique and innovative writing styles. The chapter on content creation and marketing explores how LLMs can assist with generating product descriptions, blog posts, and social media content, streamlining your content creation process. Part 3: Evaluation and Considerations delves into the process of evaluating LLM-generated text. You'll learn about various metrics used to assess the quality of the generated content, including coherence, fluency, and grammatical correctness. The chapter on challenges and biases in LLM text generation explores the importance of mitigating bias and promoting fairness in AI development. It also addresses safety and security concerns, along with the need for explainability and interpretability of LLM outputs. The final chapter explores the exciting future of generative AI and LLMs. You'll discover emerging trends like more powerful LLMs, multimodal capabilities that integrate text with other formats, and the potential for personalized LLMs that adapt to individual users. The book concludes by discussing the broader impact of generative AI on society, exploring its potential to transform creative industries, education, and communication. By understanding the fundamentals of LLMs and their applications, you can become an active participant in this evolving landscape of AI-powered text generation. This book equips you with the knowledge and tools to leverage the power of LLMs and unlock their potential for creative exploration, informative content creation, and innovative communication.

Generative AI and LLMs
Generative AI and LLMs

Author: S. Balasubramaniam

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2024-09-23

Total Pages: 366

ISBN-13: 3111425517

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Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.

Generative AI for Cloud Solutions
Generative AI for Cloud Solutions

Author: Paul Singh

Publisher: Packt Publishing Ltd

Published: 2024-04-22

Total Pages: 301

ISBN-13: 1835080162

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Explore Generative AI, the engine behind ChatGPT, and delve into topics like LLM-infused frameworks, autonomous agents, and responsible innovation, to gain valuable insights into the future of AI Key Features Gain foundational GenAI knowledge and understand how to scale GenAI/ChatGPT in the cloud Understand advanced techniques for customizing LLMs for organizations via fine-tuning, prompt engineering, and responsible AI Peek into the future to explore emerging trends like multimodal AI and autonomous agents Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms. This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you’ll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you’ll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements. By the end of this book, you’ll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services.What you will learn Get started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function together Understand how we started applying NLP to concepts like transformers Grasp the process of fine-tuning and developing apps based on RAG Explore effective prompt engineering strategies Acquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIs Discover how to scale and secure GenAI systems, while understanding the principles of responsible AI Who this book is for This artificial intelligence book is for aspiring cloud architects, data analysts, cloud developers, data scientists, AI researchers, technical business leaders, and technology evangelists looking to understanding the interplay between GenAI and cloud computing. Some chapters provide a broad overview of GenAI, which are suitable for readers with basic to no prior AI experience, aspiring to harness AI's potential. Other chapters delve into technical concepts that require intermediate data and AI skills. A basic understanding of a cloud ecosystem is required to get the most out of this book.

Generative AI with LangChain
Generative AI with LangChain

Author: Ben Auffarth

Publisher: Packt Publishing Ltd

Published: 2023-12-22

Total Pages: 369

ISBN-13: 1835088368

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2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Large Language Models
Large Language Models

Author: Anand Vemula

Publisher: Independently Published

Published: 2024-08-06

Total Pages: 0

ISBN-13:

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"Large Language Models: A Step-by-Step Do It Yourself Guide" is an essential resource for those looking to understand and develop large language models (LLMs) from scratch. This comprehensive guide takes readers through the entire process, from foundational concepts to advanced techniques, ensuring a thorough understanding of both the theory and practical application of LLMs. The book begins with an introduction to LLMs, covering their definitions, historical evolution, and key concepts. It explores various applications, including natural language processing, conversational AI, and text generation. Ethical considerations, such as bias and privacy, are also addressed, setting the stage for responsible AI development. In the next section, readers are guided through the process of building their own LLMs. This includes setting up the development environment, understanding essential machine learning concepts, and collecting and preparing data. Detailed tutorials on model architecture and design follow, including insights into transformers, attention mechanisms, and custom model design. Training strategies and techniques are discussed, with practical examples of fine-tuning and transfer learning. The book then shifts focus to deployment and practical use. It covers various deployment strategies, integrating LLMs with applications and services, and best practices for monitoring and maintaining models. Hands-on projects such as creating chatbots, text summarization tools, and personalized recommendation systems are included, offering readers real-world experience. Advanced topics, including innovative training methods and case studies, round out the guide. Real-world examples, like implementing customer support bots and automating content generation, provide valuable insights into practical applications of LLMs. Overall, this guide equips readers with the knowledge and skills needed to build, deploy, and optimize their own large language models, making it an indispensable resource for AI enthusiasts and professionals alike.

Prompt Engineering for Generative AI
Prompt Engineering for Generative AI

Author: James Phoenix

Publisher: "O'Reilly Media, Inc."

Published: 2024-05-16

Total Pages: 423

ISBN-13: 1098153405

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Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

LLMs and Generative AI for Healthcare
LLMs and Generative AI for Healthcare

Author: Kerrie Holley

Publisher: "O'Reilly Media, Inc."

Published: 2024-08-20

Total Pages: 218

ISBN-13: 1098160886

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Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley, former Google healthcare professionals, guide you through the transformative potential of large language models (LLMs) and generative AI in healthcare. From personalized patient care and clinical decision support to drug discovery and public health applications, this comprehensive exploration covers real-world uses and future possibilities of LLMs and generative AI in healthcare. With this book, you will: Understand the promise and challenges of LLMs in healthcare Learn the inner workings of LLMs and generative AI Explore automation of healthcare use cases for improved operations and patient care using LLMs Dive into patient experiences and clinical decision-making using generative AI Review future applications in pharmaceutical R&D, public health, and genomics Understand ethical considerations and responsible development of LLMs in healthcare "The authors illustrate generative's impact on drug development, presenting real-world examples of its ability to accelerate processes and improve outcomes across the pharmaceutical industry."--Harsh Pandey, VP, Data Analytics & Business Insights, Medidata-Dassault Kerrie Holley is a retired Google tech executive, IBM Fellow, and VP/CTO at Cisco. Holley's extensive experience includes serving as the first Technology Fellow at United Health Group (UHG), Optum, where he focused on advancing and applying AI, deep learning, and natural language processing in healthcare. Manish Mathur brings over two decades of expertise at the crossroads of healthcare and technology. A former executive at Google and Johnson & Johnson, he now serves as an independent consultant and advisor. He guides payers, providers, and life sciences companies in crafting cutting-edge healthcare solutions.

Adversarial AI Attacks, Mitigations, and Defense Strategies
Adversarial AI Attacks, Mitigations, and Defense Strategies

Author: John Sotiropoulos

Publisher: Packt Publishing Ltd

Published: 2024-07-26

Total Pages: 586

ISBN-13: 1835088678

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Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features Understand the connection between AI and security by learning about adversarial AI attacks Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you’ll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems effectively.What you will learn Understand poisoning, evasion, and privacy attacks and how to mitigate them Discover how GANs can be used for attacks and deepfakes Explore how LLMs change security, prompt injections, and data exposure Master techniques to poison LLMs with RAG, embeddings, and fine-tuning Explore supply-chain threats and the challenges of open-access LLMs Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.

Generative AI and LLMs
Generative AI and LLMs

Author: S. Balasubramaniam

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2024-09-23

Total Pages: 290

ISBN-13: 311142507X

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Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.

Unlocking Data with Generative AI and RAG
Unlocking Data with Generative AI and RAG

Author: Keith Bourne

Publisher: Packt Publishing Ltd

Published: 2024-09-27

Total Pages: 346

ISBN-13: 1835887910

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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.