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Unlock the power of generative AI in Python development and learn how you can enhance your coding speed, quality, and efficiency with real-world examples and hands-on strategies

Key Features

• Discover how GitHub Copilot, ChatGPT, and the OpenAI API can boost your coding productivity
• Push beyond the basics to apply advanced techniques across the software development lifecycle
• Master best practices and advanced techniques to achieve quality code for even complex tasks
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Work with GitHub Copilot in PyCharm, VS Code, and Jupyter Notebook
• Apply advanced prompting methods with ChatGPT and OpenAI API
• Gain insight into GenAI fundamentals to achieve better outcomes
• Adopt our structured framework to produce high-quality code
• Find out how to select the optimal GenAI tool for solving your specific tasks
• Elevate your use of GenAI tools from debugging to delivery
• Join the next generation of supercharged software engineers

Who this book is for

If you are a Python developer curious about GenAI and are looking to elevate your software engineering productivity, Supercharged Coding with GenAI will transform your approach to software. Covering various structured examples of varying problem complexities that showcase the use of advanced prompting techniques, this book is suitable for early intermediate through advanced developers. To get the most out of this book, you should have at least one year of hands-on Python development experience and be somewhat familiar with the SDLC.

Table of Contents

1. From Automation to Full Software Development Life Cycle: The Current Opportunity for GenAI
2. Your Quickstart Guide to OpenAI API
3. A Guide to GitHub Copilot with PyCharm, VS Code, and Jupyter Notebook
4. Best Practices for Prompting with ChatGPT
5. Best Practices for Prompting with OpenAI API and GitHub Copilot
6. Behind the Scenes: How ChatGPT, GitHub Copilot, and Other LLMs Work
7. Reading and Understanding Code Bases with GenAI
8. An Introduction to Prompt Engineering
9. Advanced Prompt Engineering for Coding-Related Tasks
10. Refactoring Code with GenAI
11. Fine-Tuning Models with OpenAI

(N.B. Please use the Read Sample option to see further chapters)

Hila Paz Herszfang, with seven years of building machine learning (ML) services and leading teams, holds a master's degree in information management systems and is completing a second master's in data science, both from Harvard Extension School. She developed a Python for MLOps Udemy course and runs a math and tech TikTok channel boasting 15K followers and 300K+ likes.

Peter V. Henstock is an AI expert with 25+ years of experience at Pfizer, Incyte, and MIT LL. He teaches graduate software engineering and AI/ML courses at Harvard Extension School. He holds a PhD in AI from Purdue and seven Master's degrees. Recognized as a top AI leader by DKA, Peter guides professionals in AI/ML, software, visualization, and statistics.