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.
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.
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.
Author Tom Taulli provides a methodology for modular programming that aligns effectively with the way prompts create AI-generated code.
You\'ll also learn about more specialized generative AI tools for tasks such as text-to-image creation.
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).
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
