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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 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 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. *Email sign-up and proof of purchase required
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

Introducing time and space analysis

In our current technology cycle, most companies are leveraging big data and artificial intelligence to improve their businesses. For example, videos on the YouTube and TikTok platforms are viewed over a billion times per day which generates recommendations and advertising value. In these cases, even 1% gains in efficiency for storage or computation may save millions of dollars.

According to Lambda Labs, training the generative pre-trained transformer model GPT-3 involved about 700 gigabytes of Random Access Memory (RAM) and thousands of powerful Nvidia GPUs operating in parallel, with a cumulative compute time equivalent to 355 years. Even a minor inefficiency in implementing these models could lead to substantial costs or, in the worst case, create a bottleneck that would prevent the model from running effectively.

The runtime of a program

The runtime of a program refers to the time it takes to execute the tasks defined in the source...

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Supercharged Coding with GenAI
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