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Autor(en): 
  • Taulli Tom
  • Ai-Assisted Programming: Better Planning, Coding, Testing, and Deployment 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  April 2024  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / Computer programming / software engineering / COMPUTERS / Artificial Intelligence / General / COMPUTERS / Data Science / Machine Learning / COMPUTERS / Internet / Web Programming / COMPUTERS / Languages / General / COMPUTERS / Software Development & Engineering / Tools / machine learning
    ISBN:  9781098164560 
    EAN-Code: 
    9781098164560 
    Verlag:  O'Reilly 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 233 mm / B 178 mm / D 13 mm 
    Gewicht:  395 gr 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    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 tools
    • Pros, cons, and use cases of popular systems such as GitHub Copilot and Amazon CodeWhisperer
    • Ways to use ChatGPT, Gemini, 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, starter code, object-oriented programming classes, and GitHub Actions
    • >

      



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