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Supercharged Coding with GenAI

Supercharged Coding with GenAI

By : Hila Paz Herszfang, Peter V. Henstock
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Supercharged Coding with GenAI

Supercharged Coding with GenAI

5 (1)
By: Hila Paz Herszfang, Peter V. Henstock

Overview of this book

Software development is being transformed by GenAI tools, such as ChatGPT, OpenAI API, and GitHub Copilot, redefining how developers work. This book will help you become a power user of GenAI for Python code generation, enabling you to write better software faster. Written by an ML advisor with a thriving tech social media presence and a top AI leader who brings Harvard-level instruction to the table, this book combines practical industry insights with academic expertise. With this book, you'll gain a deep understanding of large language models (LLMs) and develop a systematic approach to solving complex tasks with AI. Through real-world examples and practical exercises, you’ll master best practices for leveraging GenAI, including prompt engineering techniques like few-shot learning and Chain-of-Thought (CoT). Going beyond simple code generation, this book teaches you how to automate debugging, refactoring, performance optimization, testing, and monitoring. By applying reusable prompt frameworks and AI-driven workflows, you’ll streamline your software development lifecycle (SDLC) and produce high-quality, well-structured code. By the end of this book, you'll know how to select the right AI tool for each task, boost efficiency, and anticipate your next coding moves—helping you stay ahead in the AI-powered development era.
Table of Contents (23 chapters)
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1
Part 1: Foundations for Coding with GenAI
7
Part 2: Basics to Advanced LLM Prompting for GenAI Coding
14
Part 3: From Code to Production with GenAI
21
Index

Finding outdated docstrings with GitHub Copilot, ChatGPT, and OpenAI API

A frequent debate among software engineers is whether documentation should be written when writing the code or later, when the code has stabilized. One line of thinking is that the docstring (and test cases) should be written at the same time. The primary argument is that the intention of the code is clearest when the developer is working through its logic and understands its purpose. The problem is that, as code is routinely refactored, the docstring comments and test cases must be adapted, making the original versions obsolete. The other line of thinking is that the documentation can and should be written later, once the code settles, to minimize the repeated rewriting of the comments.

New GenAI technology makes the debate irrelevant since it offers two solutions. The first solution is simply to regenerate and update all the comments at the method or file level. The previous section described this strategy...

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Supercharged Coding with GenAI
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