This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading. FEATURES Offers an all-encompassing view of ChatGPT and GPT-4, from basics to advanced topics, including functionalities, capabilities, and limitations Contains Python 3.x code samples demonstrating the application of GPT-4 in real-world scenarios Provides a forward-looking perspective on Generative AI and its integration with data visualization and DALL-E Includes companion files with source code, data sets, and figures

Use groundbreaking generative AI tools to increase your productivity, efficiency, and code quality. AI coding tools like ChatGPT and GitHub Copilot are changing the way we write code and build software. AI-Powered Developer reveals the practical best practices you need to deliver reliable results with AI. It cuts through the hype, showcasing real-world examples of how these tools ease and enhance your everyday tasks, and make you more creative. In AI-Powered Developer you’ll discover how to get the most out of AI: • Harness AI to help you design and plan software • Use AI for code generation, debugging, and documentation • Improve your code quality assessments with the help of AI • Articulate complex problems to prompt an AI solution • Develop a continuous learning mindset that keeps you up to date • Adapt your development skills to almost any language AI coding tools give you a smart and reliable junior developer that’s fast and keen to help out with your every task and query. AI-Powered Developer helps you put your new assistant to work. You’ll learn to use AI for everything from writing boilerplate, to testing and quality assessment, managing infrastructure, delivering security, and even assisting with software design. About the technology Using AI tools like Copilot and ChatGPT is like hiring a super-smart and super-fast junior developer eager to take on anything from research to refactoring. Coding with AI can help you work faster, write better applications, and maybe do things that aren’t even possible with your current team. This book will show you how. About the book AI-Powered Developer: Build software with ChatGPT and Copilot teaches you in concrete detail how to maximize the impact of AI coding tools in real-world software development. In it, you’ll walk through a complete application, introducing AI into every step of the workflow. You’ll use ChatGPT and Copilot to generate code and ideas, make predictive suggestions, and develop a self-documenting application. You’ll also learn how AI can help test and explain your code. What's inside • Use AI to design and plan software • Code generation, debugging, and documentation • Improve code quality assessments • Work with unfamiliar programming languages About the reader For intermediate software developers. No AI experience necessary. About the author Nathan B. Crocker is Cofounder and CTO at Checker Corp. The technical editor on this book was Nicolai Nielsen. Table of Contents PART 1 1 Understanding large language models 2 Getting started with large language models PART 2 3 Designing software with ChatGPT 4 Building software with GitHub Copilot 5 Managing data with GitHub Copilot and Copilot Chat PART 3 6 Testing, assessing, and explaining with large language models PART 4 7 Coding infrastructure and managing deployments 8 Secure application development with ChatGPT 9 GPT-ing on the go A Setting up ChatGPT B Setting up GitHub Copilot C Setting up AWS CodeWhisperer

This minibook is a comprehensive guide for Python developers who want to learn how to build applications with large language models. Authors Olivier Caelen and Marie-Alice Blete cover the main features and benefits of GPT-4 and ChatGPT and explain how they work. You'll also get a step-by-step guide for developing applications using the GPT-4 and ChatGPT Python library, including text generation, Q&A, and content summarization tools. Written in clear and concise language, Developing Apps with GPT-4 and ChatGPT includes easy-to-follow examples to help you understand and apply the concepts to your projects. Python code examples are available in a GitHub repository, and the book includes a glossary of key terms. Ready to harness the power of large language models in your applications? This book is a must. You'll learn: The fundamentals and benefits of ChatGPT and GPT-4 and how they work How to integrate these models into Python-based applications for NLP tasks How to develop applications using GPT-4 or ChatGPT APIs in Python for text generation, question answering, and content summarization, among other tasks Advanced GPT topics including prompt engineering, fine-tuning models for specific tasks, plug-ins, LangChain, and more

Leverage Amazon Q Developer to boost productivity and maximize efficiency by accelerating software development life cycle tasks Key Features First book on the market to thoroughly explore all of Amazon Q Developer’s features Gain an understanding of Amazon Q Developer's capabilities across the software development life cycle through real-world examples Build apps with Amazon Q Developer by auto-generating code in various languages within supported IDEs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany developers face the challenge of managing repetitive tasks and maintaining productivity. This book will help you tackle both these challenges with Amazon Q Developer, a generative AI-powered assistant designed to optimize coding and streamline workflows. This book takes you through the setup and customization of Amazon Q Developer, demonstrating how to leverage its capabilities for auto-code generation, code explanation, and transformation across multiple IDEs and programming languages. You'll learn to use Amazon Q Developer to enhance coding experiences, generate accurate code references, and ensure security by scanning for vulnerabilities. The book also shows you how to use Amazon Q Developer for AWS-related tasks, including solution building, applying architecture best practices, and troubleshooting errors. Each chapter provides practical insights and step-by-step guidance to help you fully integrate this powerful tool into your development process. You’ll get to grips with effortless code implementation, explanation, transformation, and documentation, helping you create applications faster and improve your development experience. By the end of this book, you’ll have mastered Amazon Q Developer to accelerate your software development lifecycle, improve code quality, and build applications faster and more efficiently.What you will learn Understand the importance of generative AI-powered assistants in developers' daily work Enable Amazon Q Developer for IDEs and with AWS services to leverage code suggestions Customize Amazon Q Developer to align with organizational coding standards Utilize Amazon Q Developer for code explanation, transformation, and feature development Understand code references and scan for code security issues using Amazon Q Developer Accelerate building solutions and troubleshooting errors on AWS Who this book is for This book is for coders, software developers, application builders, data engineers, and technical resources using AWS services looking to leverage Amazon Q Developer's features to enhance productivity and accelerate business outcomes. Basic coding skills are needed to understand the concepts covered in this book.

Explore the dynamic field of LLM prompt engineering with this book. Starting with fundamental NLP principles & progressing to sophisticated prompt engineering methods, this book serves as the perfect comprehensive guide. Key Features In-depth coverage of prompt engineering from basics to advanced techniques. Insights into cutting-edge methods like AutoCoT and transfer learning. Comprehensive resource sections including prompt databases and tools. Book Description"LLM Prompt Engineering For Developers" begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering. As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology. This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.What you will learn Understand the principles of NLP and their application in LLMs. Set up and configure environments for developing with LLMs. Implement few-shot and zero-shot learning techniques. Enhance LLM outputs through AutoCoT and self-consistency methods. Apply transfer learning to adapt LLMs to new domains. Develop practical skills in testing & scoring prompt effectiveness. Who this book is for The target audience for "LLM Prompt Engineering For Developers" includes software developers, AI enthusiasts, technical team leads, advanced computer science students, and AI researchers with a basic understanding of artificial intelligence. Ideal for those looking to deepen their expertise in large language models and prompt engineering, this book serves as a practical guide for integrating advanced AI-driven projects and research into various workflows, assuming some foundational programming knowledge and familiarity with AI concepts.

Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization