AI-Assisted Programming Katowice

Get practical advice on how to leverage AI development tools for all stages of code creation, including requirements, planning, design, coding, debugging, testing, and documentation. With this book, beginners and experienced developers alike will learn how to use a wide range of tools, from general-purpose LLMs (ChatGPT, Gemini, and Claude) to code-specific systems (GitHub Copilot, Tabnine, Cursor, and Amazon CodeWhisperer).You'll also learn about more specialized generative AI tools for tasks such as text-to-image creation.Author Tom Taulli provides a methodology for modular programming that aligns effectively with the way prompts create AI-generated code. This guide also describes the best ways of using general purpose LLMs to learn a programming language, explain code, or convert code from one language to another.This book examines:The core capabilities of AI-based development toolsPros, cons, and use cases of popular systems such as GitHub Copilot and Amazon CodeWhispererWays to use ChatGPT, Gemini, Claude, and other generic LLMs for codingUsing AI development tools for the software development lifecycle, including requirements, planning, coding, debugging, and testingPrompt engineering for developmentUsing AI-assisted programming for tedious tasks like creating regular expressions, starter code, object-oriented programming classes, and GitHub ActionsHow to use AI-based low-code and no-code tools, such as to create professional UIs Spis treści: Foreword Preface Whats Covered How This Book Is Different Who Should Read This Book Conventions Used in This Book Using Code Examples OReilly Online Learning How to Contact Us Acknowledgments 1. New World for Developers Evolution and Revolution Generative AI The Benefits Minimizing Search Your Advisor IDE Integration Reflecting Your Codebase Code Integrity AI-Powered Documentation Generator Modernization Drawbacks Hallucinations Intellectual Property Privacy Security Training Data Bias A New Way for Developers Career 10x Developer? Skills of the Developer Conclusion 2. How AI Coding Technology Works Key Features Code Suggestions and Context-Aware Completions Versus Smart Code Completion Compilers Versus AI-Assisted Programming Tools Levels of Capability Generative AI and Large Language Models (LLMs) Evolution The Transformer Model OpenAI Playground Tokens Using the Platform Evaluating LLMs Types of LLMs Evaluation of AI-Assisted Programming Tools Conclusion 3. Prompt Engineering Art and Science Challenges The Prompt Context Instructions Summarization Text Classification Recommendation Translation Input of Content Format Best Practices Be Specific Acronyms and Technical Terms Zero- and Few-Shot Learning Leading Words Chain of Thought (CoT) Prompting Leading Questions Ask for Examples and Analogies Reducing Hallucinations Security and Privacy Autonomous AI Agents Conclusion 4. GitHub Copilot GitHub Copilot Pricing and Versions Use Case: Programming Hardware Use Case: Shopify Use Case: Accenture Security Getting Started Codespaces and Visual Studio Code Suggestions Comments Chat Inline Chat Open Tabs Command-Line Interface Copilot Partner Program Conclusion 5. Other AI-Assisted Programming Tools Amazons CodeWhisperer Googles Duet AI for Developers Tabnine Replit CodeGPT Cody CodeWP Warp Bito AI Cursor Code Llama Other Open Source Models StableCode AlphaCode PolyCoder CodeT5 Enterprise Software Companies Conclusion 6. ChatGPT and Other General-Purpose LLMs ChatGPT GPT-4 Navigating ChatGPT Mobile App Custom Instructions Browse with Bing Tedious Tasks Regular Expressions Starter Code GitHub README Cross-Browser Compatibility Bash Commands GitHub Actions Plugins The Codecademy Plugin The AskYourDatabase Plugin Recombinant AI Plugin GPTs Gemini Applications Gemini for Coding Claude Conclusion 7. Ideas, Planning, and Requirements Brainstorming Market Research Market Trends Total Addressable Market Competition Requirements Product Requirements Document Software Requirements Specification Interviews Whiteboarding Tone Approaches to Project Planning Test-Driven Development (TDD) Planning Web Design Conclusion 8. Coding Reality Check Judgment Calls Learning Comments Modular Programming Starting a Project Autofill Refactoring Ninja Code Extract Method Decomposing Conditionals Renaming Dead Code Functions Object-Oriented Programing Frameworks and Libraries Data Frontend Development CSS Creating Graphics AI Tools APIs Conclusion 9. Debugging, Testing, and Deployment Debugging Documentation Code Review Unit Tests Pull Requests Deployment User Feedback The Launch Conclusion 10. Takeaways The Learning Curve Is Steep There Are Major Benefits But There Are Drawbacks Prompt Engineering Is an Art and Science Beyond Programming AI Wont Take Your Job Conclusion Index O autorze: Tom Taulli tworzy oprogramowanie od ponad czterdziestu lat. Założył kilka przedsiębiorstw. Regularnie pisze felietony do takich serwisów jak Bloomberg.com, a także doradza różnym firmom działającym w dziedzinie sztucznej inteligencji i zrobotyzowanej automatyzacji procesów.