Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantageKey FeaturesOptimize data retrieval and generation using vector databasesBoost decision-making and automate workflows with AI agentsOvercome common challenges in implementing real-world RAG systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative 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 learnUnderstand RAG principles and their significance in generative AIIntegrate LLMs with internal data for enhanced operationsMaster vectorization, vector databases, and vector search techniquesDevelop skills in prompt engineering specific to RAG and design for precise AI responsesFamiliarize yourself with AI agents' roles in facilitating sophisticated RAG applicationsOvercome scalability, data quality, and integration issuesDiscover strategies for optimizing data retrieval and AI interpretabilityWho this book is forThis 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.Table of ContentsWhat Is Retrieval-Augmented Generation (RAG)Code Lab – An Entire RAG PipelinePractical Applications of RAGComponents of a RAG SystemManaging Security in RAG ApplicationsInterfacing with RAG and GradioThe Key Role Vectors and Vector Stores Play in RAGSimilarity Searching with VectorsEvaluating RAG Quantitatively and with VisualizationsKey RAG Components in LangChainUsing LangChain to Get More from RAGCombining RAG with the Power of AI Agents and LangGraphUsing Prompt Engineering to Improve RAG EffortsAdvanced RAG-Related Techniques for Improving Results Read more

Transform your professional world with ChatGPT and OpenAI—master prompt design to revolutionize development, marketing, research, and enterprise implementationKey FeaturesTurn ChatGPT into your companion for marketing, research, personal productivity, art and codingLearn prompt engineering techniques that deliver consistent, relevant, and ethical AI-powered resultsBuild custom GPTs and assistants tailored to your specific business needs and workflowsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPractical Generative AI with ChatGPT is your hands-on guide to unlocking the full potential of ChatGPT. From building AI assistants and mastering prompt engineering to analyzing documents and images and even generating code, this book equips you with the skills to integrate generative AI into your workflow.Written by a technical architect specializing in AI and intelligent applications, this book provides the tools and knowledge you need to streamline tasks, enhance productivity, and create intelligent solutions. You’ll learn how to craft precise prompts, leverage ChatGPT for daily efficiency, and develop custom AI assistants tailored to your needs.The chapters show you how to use ChatGPT’s multimodal capabilities to generate images with DALL·E and even transform images into code. This ChatGPT book goes beyond basic interactions by showing you how to design custom GPTs and integrate OpenAI’s APIs into your applications. You’ll explore how businesses use OpenAI models, from building AI applications, including semantic search, to creating an AI roadmap. Each chapter is packed with practical examples, ensuring you can apply the techniques right away.By the end of this book, you’ll be well equipped to leverage OpenAI's technology for competitive advantage.What you will learnExplore the fundamentals of generative AI and GPT modelsMaster prompt engineering to consistently get relevant and reliable outputs from ChatGPTDevelop marketing strategies and conduct meaningful A/B testing with AI assistanceBoost your coding with code generation, review, and optimizationEnhance research with real-time knowledge miningEnhance your visual creativity with image generation, image understanding, and style transferDesign custom GPTs and assistants tailored to specific business functionsDiscover how enterprises are leveraging large language models for their AI appsWho this book is forThis book is ideal for business professionals, developers, marketers, researchers, and decision-makers who want to leverage AI to enhance productivity. No advanced technical background is required for the foundational sections, making the content accessible to beginners, while later chapters provide depth for technical professionals implementing enterprise solutions. If you’re seeking practical applications of generative AI in business contexts, you’ll find immediate, actionable value in this book.Table of ContentsIntroduction to Generative AIOpenAI and ChatGPT: Beyond the Market HypeUnderstanding Prompt EngineeringBoosting Day-to-Day Productivity with ChatGPTDeveloping the Future with ChatGPTMastering Marketing with ChatGPTResearch Reinvented with ChatGPTUnleashing Creativity Visually with ChatGPTExploring GPTsLeveraging OpenAI Models for Enterprise- Scale Applications Epilogue and Final Thoughts Read more

Apply cutting-edge machine learning techniques—from crowdsourced relevance and knowledge graph learning, to Large Language Models (LLMs)—to enhance the accuracy and relevance of your search results.Delivering effective search is one of the biggest challenges you can face as an engineer. AI-Powered Search is an in-depth guide to building intelligent search systems you can be proud of. It covers the critical tools you need to automate ongoing relevance improvements within your search applications. Inside you’ll learn modern, data-science-driven search techniques like: • Semantic search using dense vector embeddings from foundation models • Retrieval augmented generation (RAG) • Question answering and summarization combining search and LLMs • Fine-tuning transformer-based LLMs • Personalized search based on user signals and vector embeddings • Collecting user behavioral signals and building signals boosting models • Semantic knowledge graphs for domain-specific learning • Semantic query parsing, query-sense disambiguation, and query intent classification • Implementing machine-learned ranking models (Learning to Rank) • Building click models to automate machine-learned ranking • Generative search, hybrid search, multimodal search, and the search frontier AI-Powered Search will help you build the kind of highly intelligent search applications demanded by modern users. Whether you’re enhancing your existing search engine or building from scratch, you’ll learn how to deliver an AI-powered service that can continuously learn from every content update, user interaction, and the hidden semantic relationships in your content. You’ll learn both how to enhance your AI systems with search and how to integrate large language models (LLMs) and other foundation models to massively accelerate the capabilities of your search technology. Foreword by Grant Ingersoll. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Modern search is more than keyword matching. Much, much more. Search that learns from user interactions, interprets intent, and takes advantage of AI tools like large language models (LLMs) can deliver highly targeted and relevant results. This book shows you how to up your search game using state-of-the-art AI algorithms, techniques, and tools. About the book AI-Powered Search teaches you to create a search that understands natural language and improves automatically the more it is used. As you work through dozens of interesting and relevant examples, you’ll learn powerful AI-based techniques like semantic search on embeddings, question answering powered by LLMs, real-time personalization, and Retrieval Augmented Generation (RAG). What's inside • Sparse lexical and embedding-based semantic search • Question answering, RAG, and summarization using LLMs • Personalized search and signals boosting models • Learning to Rank, multimodal, and hybrid search About the reader For software developers and data scientists familiar with the basics of search engine technology. About the author Trey Grainger is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. Doug Turnbull is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. Max Irwin is the Founder of Max.io and former Managing Consultant at OpenSource Connections. Table of Contents Part 1 1 Introducing AI-powered search 2 Working with natural language 3 Ranking and content-based relevance 4 Crowdsourced relevance Part 2 5 Knowledge graph learning 6 Using context to learn domain-specific language 7 Interpreting query intent through semantic search Part 3 8 Signals-boosting models 9 Personalized search 10 Learning to rank for generalizable search relevance 11 Automating learning to rank with click models 12 Overcoming ranking bias through active learning Part 4 13 Semantic search with dense vectors 14 Question answering with a fine-tuned large language model 15 Foundation models and emerging search paradigms A Running the code examples B Supported search engines and vector database Read more

This Book grants Free Access to our e-learning Platform, which includes:✅ Free Repository Code with all code blocks used in this book✅ Access to Free Chapters of all our library of programming published books✅ Free premium customer support✅ Much more...Discover the Blueprint That Transforms Curious Minds into AI BuildersWhen John first opened the OpenAI documentation, he was overwhelmed. What model should he use? How do you prompt it effectively? What even is a system message? He wasn’t looking to become an AI researcher — he just wanted to build something useful. But every tutorial he found was either too simple… or too complex.Three months later, John launched his first GPT-powered product — a personal assistant that books flights, summarizes emails, and even jokes in his tone of voice. And it all started with the same book you’re about to read.Welcome to Volume 1 of the OpenAI API Bible: Foundations of GPT and the OpenAI Ecosystem — your complete, structured, and beginner-friendly guide to mastering GPT-4o and building intelligent applications from scratch.Finally Understand How OpenAI’s API Actually WorksThis is not a theoretical book. It’s a hands-on, real-world roadmap written for developers, creators, and problem-solvers who want to:✅ Master the differences between GPT-3.5, GPT-4, GPT-4 Turbo, and GPT-4o✅ Write prompts that produce consistent, high-quality results✅ Handle conversation memory and build multi-turn chat experiences✅ Use OpenAI’s Function Calling and Responses API✅ Deploy your first chatbot using Python, Flask, and StreamlitYou’ll go beyond the playground and into production-level understanding — no fluff, no filler, just clear explanations, working code, and practical examples.Build Real Applications, Not Just Hello WorldYou won’t just learn what GPT can do — you’ll build with it. Each chapter includes guided exercises, practical walkthroughs, and working code you can customize for your own use cases.You’ll also learn how to:Design smart assistants with memoryAutomate business workflowsConnect GPT with external tools and dataCreate chat interfaces that feel humanUse best practices to manage API cost and securityThis book is designed to turn curiosity into capability — so by the time you reach the final chapter, you’ll be building, not browsing.The AI Revolution Is Happening. Be the One Who Builds With It.The future belongs to those who can understand and shape intelligent systems. Whether you’re a developer, entrepreneur, freelancer, or digital creator, GPT-4o is now your competitive edge — and this book is your step-by-step guide to unlocking it.Thousands of people will experiment with AI this year. Fewer will learn to master it. Even fewer will turn it into something the world can use.Which one will you be?John made the leap. He turned confusion into clarity, ideas into apps, and ambition into a launched product. Now it's your turn. Read more

Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applicationsKey FeaturesEmbed LLMs into real-world applicationsUse LangChain to orchestrate LLMs and their components within applicationsGrasp basic and advanced techniques of prompt engineeringBook DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities.The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio.Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learnExplore the core components of LLM architecture, including encoder-decoder blocks and embeddingsUnderstand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like LangChain, with Streamlit for the frontendGet familiar with LLM components such as memory, prompts, and toolsLearn how to use non-parametric knowledge and vector databasesUnderstand the implications of LFMs for AI research and industry applicationsCustomize your LLMs with fine tuningLearn about the ethical implications of LLM-powered applicationsWho this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics.We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.Table of ContentsIntroduction to Large Language ModelsLLMs for AI-Powered ApplicationsChoosing an LLM for Your ApplicationPrompt EngineeringEmbedding LLMs within Your ApplicationsBuilding Conversational ApplicationsSearch and Recommendation Engines with LLMsUsing LLMs with Structured DataWorking with CodeBuilding Multimodal Applications with LLMsFine-Tuning Large Language ModelsResponsible AIEmerging Trends and Innovations Read more

None

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPyKey FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook DescriptionGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence.After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications.By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.What you will learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho this book is forThis book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.Table of ContentsAn Introduction to Genetic AlgorithmsUnderstanding the Key Components of Genetic AlgorithmsUsing the DEAP FrameworkCombinatorial OptimizationConstraint SatisfactionOptimizing Continuous FunctionsEnhancing Machine Learning Models Using Feature SelectionHyperparameter Tuning Machine Learning ModelsArchitecture Optimization of Deep Learning NetworksReinforcement Learning with Genetic AlgorithmsGenetic Image ReconstructionOther Evolutionary and Bio-Inspired Computation Techniques Read more