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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 Part 2 of the book, we explored LLMs in greater depth. We explained how they work, what they excel at, and how to leverage prompt engineering techniques to achieve more effective results. We also covered strategies for evaluating their outputs to ensure reliability.

This chapter took the concept of few-shot learning a step further by demonstrating how to fine-tune an LLM to specialize on a given task. Through positive and contrastive training examples, we guided the model to generate function implementations based solely on their signatures, returning clean code without inline comments. This approach can be applied more broadly to tasks such as generating unit test suites, maintaining docstring quality, or refactoring for loops across an entire repository.

With this deeper understanding of LLMs, prompt engineering, and output evaluation, we now have the essential tools to become supercharged coders. We can determine the best tool for a given task, whether ChatGPT...

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Supercharged Coding with GenAI
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