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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

Utilizing prompt engineering for coding

In Chapter 4, we explored the three pillars of achieving quality output: model mastery, evaluation metrics, and precise prompts. We also discussed how following the five S’s best practices for prompts (structured, surrounded, single-tasked, specific, and short) can significantly enhance the quality of model output. Using OpenAI’s example of an effective prompt, we demonstrated how aligning with these principles, such as focusing exclusively on error fixes and providing a clear list of issues to address, could improve results.

As tasks grow more complex, advanced techniques are essential to guide models toward achieving desired outcomes. LLMs may need additional instructions to adhere to a specific style guide, pass a unit test suite, or fix reproducibility issues.

Since the advent of LLMs in 2020, prompt engineering has developed into a practice that refines and structures prompts to achieve better results and address more...

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