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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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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

Advanced Prompt Engineering for Coding-Related Tasks

Some coding tasks extend beyond simple, single-task initiatives. For these more complex scenarios, advanced prompt engineering techniques such as chain of thought (CoT) and chaining can be highly effective when working with large language models (LLMs). This chapter will explore how to use these techniques when working with OpenAI API, ChatGPT, and GitHub Copilot on challenges that often require more than just the initial prompt to achieve the desired output.

We will walk through an example of implementing a geometric mean calculator for portfolio returns that handles negative net returns. By applying CoT prompting, we will guide the model with explicit reasoning steps and achieve desirable results, even with a lighter model such as GPT-4o mini. Then, using chaining, we will improve the initial implementation by enhancing its type hints and refining the docstring.

This chapter covers the following topics:

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Supercharged Coding with GenAI
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