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  • Book Overview & Buying Supercharged Coding with GenAI
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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

Summary

In this chapter, we introduced few-shot learning as a prompt engineering technique to guide LLMs toward more predictable and desired outcomes. We demonstrated its application in scenarios such as implementing a specific logging structure and following a particular coding style.

We explored how to implement few-shot prompting across different GenAI applications. In ChatGPT and OpenAI API, we utilized specific keyword combinations such as question and answer or old and refactored to structure the few-shot examples. With GitHub Copilot, we leveraged a style guide file to influence code completion output.

We also introduced additional prompt engineering techniques that are valuable for scaling bug fixes. Iterative prompting enables models to refine their output by utilizing feedback from compilation checks until the code compiles successfully. Template-based prompting leads the model toward producing outputs with a specific structure. Furthermore, CoT prompting breaks...

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