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相關主題
商品描述
Get practical advice on how to leverage AI development tools for all stages of code creation, including requirements, planning, and design; coding; and debugging, testing, and documentation. With this practical book, beginners and experienced developers alike will learn how to use a wide range of tools, from general-purpose LLMs (ChatGPT, Bard, 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 tools
- Pros, cons, and use cases of popular systems such as GitHub Copilot and Amazon CodeWhisperer
- Ways to use ChatGPT, Bard, Claude, and other generic LLMs for coding
- Using AI development tools for the software development lifecycle, including requirements, planning, coding, debugging, and testing
- Prompt engineering for development
- Using AI-assisted programming for tedious tasks like creating regular expressions making chron jobs and GitHub Actions
- How to use AI-based low-code and no-code tools
商品描述(中文翻譯)
在這本實用書中，您將獲得有關如何運用人工智慧開發工具於程式碼創建的各個階段（包括需求、規劃、設計、編碼、除錯、測試和文件撰寫）的實用建議。無論是初學者還是有經驗的開發人員，都將學習如何使用各種工具，從通用的語言模型（如ChatGPT、Bard和Claude）到針對程式碼的特定系統（如GitHub Copilot、Tabnine、Cursor和Amazon CodeWhisperer）。
您還將了解更專門的生成式人工智慧工具，用於文本到圖像的創建等任務。
作者Tom Taulli提供了一種模塊化編程的方法論，與提示如何創建AI生成的程式碼的方式相互配合。本指南還描述了使用通用語言模型學習編程語言、解釋程式碼或將程式碼從一種語言轉換為另一種語言的最佳方法。
本書探討了以下內容：
- 基於人工智慧的開發工具的核心能力
- 流行系統（如GitHub Copilot和Amazon CodeWhisperer）的優缺點和使用案例
- 使用ChatGPT、Bard、Claude和其他通用語言模型進行編碼的方法
- 在軟體開發生命週期中使用人工智慧開發工具，包括需求、規劃、編碼、除錯和測試
- 開發中的提示工程
- 使用人工智慧輔助編程處理繁瑣任務，如創建正則表達式、製作定時任務和GitHub Actions
- 如何使用基於人工智慧的低代碼和無代碼工具
