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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 explored how to achieve desirable outcomes from LLMs by effectively applying CoT and chaining for coding tasks with an extended scope.

With CoT prompting, we saw how introducing reasoning steps into our prompts enables the model to handle more nuanced challenges, such as implementing a geometric mean function that supports negative net returns. We used function names as intermediate reasoning steps, while relying on Copilot, ChatGPT, and OpenAI API to fill in the implementation details.

Through chaining, we began with an initial implementation that is functionally correct and iteratively improved by adding type hints and refining docstrings. When using OpenAI API, we introduced a selective history approach to make chaining more efficient, which still holds as the chain of tasks gets longer.

In the next chapter, we will delve deeper into refactoring code with GenAI applications. Later in the book, we will introduce advanced prompt engineering...

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