Search results for "Mastering Prompt Engineering for Generative AI"
Items we need to order for you
Prompt Engineering
In a world where communication is key, prompt engineering emerges as a transformative approach to enhancing interaction, creativity, and innovation. From business and healthcare to education, law, and beyond, prompt engineering offers a versatile toolkit for navigating complex challenges and driving meaningful change.This comprehensive guide delves into the intricacies of prompt engineering, providing insights, techniques, and practical examples for leveraging prompts effectively. It explores the evolution of prompt engineering, from its early antecedents to its contemporary applications with advanced language models like ChatGPT. You will discover how prompts can enhance communication, foster creativity, facilitate problem-solving, and empower professionals across diverse domains.Whether you're a seasoned practitioner, an aspiring enthusiast, or a curious explorer, this book is your gateway to unlocking the full potential of prompt engineering. Join us on this journey of discovery and innovation as we harness the power of prompts to shape a brighter future.
R 3,609.56
Empowering the Public Sector with Generative AI
This is your guide book to Generative AI (GenAI) and its application in addressing real-world challenges within the public sector. The book addresses a range of topics from GenAI concepts and strategy to public sector use cases, architecture patterns, and implementation best practices. With a general background in technology and the public sector, you will be able to understand the concepts in this book.The book will help you develop a deeper understanding of GenAI and learn how GenAI differs from traditional AI. You will explore best practices such as prompt engineering, and fine-tuning, and architectural patterns such as Retrieval Augmented Generation (RAG). And you will discover specific nuances, considerations, and strategies for implementation in a public sector organization. You will understand how to apply these concepts in a public sector setting and address industry-specific challenges and problems by studying a variety of use cases included in the book in the areas of content generation, chatbots, summarization, and program management. What You Will LearnGenAI concepts and how GenAI differs from traditional AI/ML Prompt engineering, fine-tuning, RAG, and customizing foundation modelsStrategy, methodologies, and frameworks for the public sectorPublic sector use cases in the areas of content generation, summarization, and chatbots, plus program management, analytics, business intelligence, and reportingArchitecture and design patterns Implementation, operations, and maintenance of GenAI applications Who This Book Is ForTechnology and business leaders in the public sector who are new to AI/ML and are keen on exploring and harnessing the potential of Generative AI in their respective organizations.
R 1,140.00
R 580.00
R 751.94
R 425.00
R 855.00
R 305.00
R 489.00
R 489.00
AI Engineering
Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).
R 1,978.00
R 2,442.00
R 429.00
R 580.00
R 397.00
R 611.00
R 428.00
R 580.00
R 611.00
R 631.00
R 1,038.00
R 397.00
R 763.00
Modern Generative AI with ChatGPT and OpenAI Models
Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAIPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore the theory behind generative AI models and the road to GPT3 and GPT4Become familiar with ChatGPT's applications to boost everyday productivityLearn to embed OpenAI models into applications using lightweight frameworks like LangChainBook DescriptionGenerative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects.What you will learnUnderstand generative AI concepts from basic to intermediate levelFocus on the GPT architecture for generative AI modelsMaximize ChatGPT's value with an effective prompt designExplore applications and use cases of ChatGPTUse OpenAI models and features via API callsBuild and deploy generative AI systems with PythonLeverage Azure infrastructure for enterprise-level use casesEnsure responsible AI and ethics in generative AI systemsWho this book is forThis book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models.A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.
R 1,693.00
R 519.00
R 611.00
Applied Generative AI for Beginners
This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI.Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You’ll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains.Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights.What You Will LearnGain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google BardImplement large language models using Sklearn, complete with code examples and best practices for real-world applicationLearn how to integrate LLM’s in enterprises, including aspects like LLMOps and technology stack selectionUnderstand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insightsWho This Book Is ForData scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.
R 1,832.00
Generative Artificial Intelligence and Explainable Artificial Intelligence for Real-world Applications
This book integrates the concepts of explainable artificial intelligence, and generative explainable artificial intelligence, showcasing how explainability can be incorporated into generative models. It highlights the importance of making generative models transparent, offering strategies to achieve this through interpretability techniques.Focuses on the principles and methodologies of explainable artificial intelligence, providing readers with a thorough understanding of how artificial intelligence models can be made transparent and interpretable.Explores explainable artificial intelligence techniques, including feature importance, SHAP values, LIME, and counterfactual explanations.Offers an in-depth examination of generative artificial intelligence, covering cutting-edge advancements in generative models like GANs, VAEs, and transformer-based architectures.Bridges the gap between theoretical concepts and their practical applications, making advanced artificial intelligence technologies accessible to the readers.Includes numerous case studies and real-world examples that demonstrate the successful application of generative artificial intelligence, and explainable artificial intelligence.The text is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, biomedical engineering, and information technology.
R 10,257.00
Practical MLflow for Generative AI on Databricks
Bridge the gap between cutting-edge research and the practical operationalization of generative AI. This handy guide demystifies the complexities of managing GenAI applications using leading open source platform MLflow on Databricks.What sets this book apart is its focus on the requirements of managing and deploying GenAI applications. Authors Nuwan Ganganath, Julie Nguyen, and Chang Shi Lim—seasoned ML and AI specialists from Databricks—take you through each stage of the development of an agentic chatbot, sharing best practices, innovative techniques, and common challenges along the way. By combining deep technical insights with interactive exercises and actionable real-world code examples, they ensure that you're equipped to implement robust GenAI applications in modern enterprise environments.Understand MLflow fundamentals, including core components and the tool's application to GenAI on the Databricks platformBuild, track, and manage GenAI applications using MLflow, from prompts to the overall agentic chainOptimize and evaluate GenAI applications through a combination of automated LLM-assisted judges and human-in-the-loop reviewDeploy production-ready GenAI applications, leveraging Databricks' scalable infrastructure and monitoring capability
R 1,978.00
Generative AI with Amazon Bedrock
Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standardsKey FeaturesLearn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist ArchitectsMaster the core techniques to develop and deploy several AI applications at scaleGo beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricksPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure.Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock.By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learnExplore the generative AI landscape and foundation models in Amazon BedrockFine-tune generative models to improve their performanceExplore several architecture patterns for different business use casesGain insights into ethical AI practices, model governance, and risk mitigation strategiesEnhance your skills in employing agents to develop intelligence and orchestrate tasksMonitor and understand metrics and Amazon Bedrock model responseExplore various industrial use cases and architectures to solve real-world business problems using RAGStay on top of architectural best practices and industry standardsWho this book is forThis book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected.
R 1,465.00
Exploring Generative AI with Computational Intelligence
This book demystifies the concept of AI-driven creativity with an in-depth understanding of the underlying principles, algorithms, and applications of generative AI. It begins with a coverage of AI fundamentals, followed by a technical discussion including detailed explanations of model architectures, training processes, and optimisation techniques. It explores case studies showcasing how generative AI is reshaping industries such as entertainment, marketing, design, and healthcare. The book also tackles ethical considerations, addressing issues related to bias, transparency, and responsible AI development.Features:Provides deep understanding of generative AI, including in-depth coverage of various generative models such as GANs, VAEs, and RNNs.Includes practical, real-world examples of how generative AI is applied in different industries.Dedicates significant attention to ethical considerations.Equips readers with the knowledge and tools to build, train, and deploy their own generative AI systems.Offers thorough explanations of AI algorithms, neural network architectures, and training processes.This book is aimed at graduate students and researchers in computer engineering, AI and ML.
R 7,584.84
R 458.00
R 305.00
Communication Skills for Generative AI
Communication Skills for Generative AI: A Practical Guide for Humans is the first comprehensive book to treat communication with generative AI as a distinct human competence—analogous to digital literacy, media literacy, or academic writing skills. Instead of framing the field through the narrow lens of “prompt engineering,” this book introduces a broader, deeper, and more natural paradigm: human–AI communication. It argues that as generative AI becomes embedded in daily life, professional work, research, education, and creative practices, humans must learn not merely to instruct AI systems but to communicate with them effectively, collaboratively, and safely. The book covers the foundations, principles, techniques, and future directions of communicating with modern AI systems. It begins by explaining why the rise of generative AI requires a new set of human capabilities, then outlines the psychological, linguistic, cognitive, and interactional mechanisms that shape human–AI dialogue. It provides a systematic structure of principles (clarity, intent, iteration, cognitive alignment, ethical responsibility) and practical techniques (contextual communication, constraint-based approaches, multimodality, iterative refinement, tone and persona control). The book also examines pitfalls and sources of misunderstanding—such as ambiguity, hallucination, overtrust, and guardrails—and offers strategies for detecting and resolving them. A central contribution of the book is its treatment of collaborative and co-creative communication, where AI is understood not as a passive tool but as an active cognitive partner capable of supporting reasoning, generating ideas, and co-creating text, visuals, and solutions. Another unique perspective is the chapter on metacommunication, which teaches readers how to talk about the communication process itself— expressing preferences, adjusting strategies, and aligning expectations during the dialogue. The book concludes with a forward-looking exploration of the future of AI communication: post-prompting paradigms, persistent personal assistants, intuitive and emotional interfaces, hybrid reasoning models, and the evolving competencies humans will need as AI becomes increasingly autonomous and embedded. This book relates to existing literature on prompt engineering, human–computer interaction, and AI literacy but significantly expands beyond them. While traditional books focus on crafting prompts or operating tools, this work builds a conceptual and methodological foundation for treating AI as a communicative partner. It introduces a unified framework for understanding interaction across text, multimodal interfaces, and emerging real-time systems. This broader perspective positions the book as a pioneering contribution that addresses an urgent gap in current academic, professional, and educational discourse.
R 2,015.00
Hands-On Generative AI with Transformers and Diffusion Models
Learn how to use generative media techniques with AI to create novel images or music in this practical, hands-on guide. Data scientists and software engineers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to your needs, and how to combine existing building blocks to create new models and creative applications in different domains.This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work.Learn the fundamentals of classic and modern generative AI techniquesBuild and customize models that can generate text, images, and soundExplore trade-offs between training from scratch and using large, pretrained modelsCreate models that can modify images by transferring the style of other imagesTweak and bend transformers and diffusion models for creative purposesTrain a model that can write text based on your styleDeploy models as interactive demos or services
R 1,824.00
Generative AI Application Integration Patterns
Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations.Key FeaturesGet familiar with the most important tools and concepts used in real scenarios to design GenAI appsInteract with GenAI models to tailor model behavior to minimize hallucinationsGet acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applicationsBook DescriptionExplore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.What you will learnConcepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentationPatterns for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical use: bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI modelsWho this book is forThis book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most include:Developer engineers with foundational tech knowledgeSoftware architects seeking best practices and design patternsProfessionals using ML for data science, research, etc., who want a deeper understanding of Generative AITechnical product managers with a software development backgroundThis concise focus ensures practical, actionable insights
R 1,693.00
