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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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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

Extracting properties from Python objects for OpenAI API

When using OpenAI for coding tasks, we often include details about Python objects such as variables, functions, and classes in our prompts. This helps with various tasks such as debugging, generating docstrings, creating unit test suites, or improving code. The information provided may include the object name, arguments, return type, filename, docstring, and source code.

Throughout this book, we frequently use the built-in inspect package to extract relevant details from Python objects. Additionally, we leverage some special attributes, identified by a double underscore (dunder), such as obj.__attribute__, which store metadata about the object or class. Here are some more examples:

  • obj.__name__ retrieves the name of the object
  • obj.__class__.__name__ retrieves the class name of an instance or function or the metaclass name of a class
  • obj.__doc__ retrieves the docstring of the object

To extract...

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