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Supercharged Coding with GenAI

Supercharged Coding with GenAI

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

Summary

In this chapter, we explored how to apply the five S’s best practices for crafting precise prompts using both OpenAI API and GitHub Copilot. With OpenAI API, we learned how to extract valuable information from Python objects, including attributes such as source code, docstrings, filenames, object types, and more. When working with Copilot, we examined which aspects of the five S’s are already implemented and identified how we can use those more precisely to improve outputs from the GenAI application.

Through two interactive labs, we utilized the Singleton design pattern, commonly employed by Python developers to enforce a single class instance with a designated metaclass. Using OpenAI API, we generated documentation for the __call__ method of the Singleton. With GitHub Copilot, we constructed a unit test that failed due to an undesirable behavior caused by faulty implementation, analyzed the test failure, and assisted Copilot in fixing the implementation...

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Supercharged Coding with GenAI
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