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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 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 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. *Email sign-up and proof of purchase required
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

Sample data creation with GenAI

One aspect of testing is the availability of data that is representative of the kind you will encounter in the wild. If you have been involved in creating mock data in years past, you will certainly appreciate GenAI approaches to this problem. The new techniques can create a variety of types of data with as specific a prompt as you need. For example, the following prompt works in either ChatGPT or GitHub Copilot:

create a table with 10 rows and the following columns:
5 digit integer id called "ID"
first name
last name
address consisting of a number, street name, and whether it is a st., dr. or ave.
city
country
disease
age

The resulting table output is shown in Figure 13.13, which includes typical names, address formats, diseases, and so on. This kind of data can be saved to a file and used for testing. Although such data could be generated on the fly, the purpose of unit testing is to create repeatable tests that achieve consistent...

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Supercharged Coding with GenAI
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