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  • Book Overview & Buying Coding with ChatGPT and Other LLMs
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Coding with ChatGPT and Other LLMs

Coding with ChatGPT and Other LLMs

By : Dr. Vincent Austin Hall, Dr. Vincent Austin Hall, Chigbo Uzokwelu
4 (4)
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Coding with ChatGPT and Other LLMs

Coding with ChatGPT and Other LLMs

4 (4)
By: Dr. Vincent Austin Hall, Dr. Vincent Austin Hall, Chigbo Uzokwelu
Buy this Book

Overview of this book

Keeping up with the AI revolution and its application in coding can be challenging, but with guidance from AI and ML expert Dr. Vincent Hall—who holds a PhD in machine learning and has extensive experience in licensed software development—this book helps both new and experienced coders to quickly adopt best practices and stay relevant in the field. You’ll learn how to use LLMs such as ChatGPT and Bard to produce efficient, explainable, and shareable code and discover techniques to maximize the potential of LLMs. The book focuses on integrated development environments (IDEs) and provides tips to avoid pitfalls, such as bias and unexplainable code, to accelerate your coding speed. You’ll master advanced coding applications with LLMs, including refactoring, debugging, and optimization, while examining ethical considerations, biases, and legal implications. You’ll also use cutting-edge tools for code generation, architecting, description, and testing to avoid legal hassles while advancing your career. By the end of this book, you’ll be well-prepared for future innovations in AI-driven software development, with the ability to anticipate emerging LLM technologies and generate ideas that shape the future of development.
Table of Contents (19 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Conventions used
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Get in touch
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Share your thoughts
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Download a free PDF copy of this book
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1
Part 1: Introduction to LLMs and Their Applications
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Part 1: Introduction to LLMs and Their Applications
2
Chapter 1: What is ChatGPT and What are LLMs?
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Chapter 1: What is ChatGPT and What are LLMs?
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Introduction to LLMs
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Origins of LLMs
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Early LLMs
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Exploring modern LLMs
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How Transformers work
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A note on the mathematics of LLMs
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Applications of LLMs
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Summary
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Bibliography
3
Chapter 2: Unleashing the Power of LLMs for Coding: A Paradigm Shift
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Chapter 2: Unleashing the Power of LLMs for Coding: A Paradigm Shift
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Technical requirements
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Unveiling the advantages of coding with LLMs
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Planning your LLM-powered coding
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Getting into LLM-powered coding
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Making it work for you
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Summary
4
Chapter 3: Code Refactoring, Debugging, and Optimization: A Practical Guide
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Chapter 3: Code Refactoring, Debugging, and Optimization: A Practical Guide
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Technical requirements
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Dealing with error codes – debugging
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Refactoring code
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Documenting code
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Testing code
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Virtual software companies
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Summary
5
Part 2: Be Wary of the Dark Side of LLM-Powered Coding
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Part 2: Be Wary of the Dark Side of LLM-Powered Coding
6
Chapter 4: Demystifying Generated Code for Readability
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Chapter 4: Demystifying Generated Code for Readability
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Technical requirements
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Generating more readable code
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What makes code hard or easy to read?
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Summarizing code for understanding
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Generating documentation
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Summary
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Bibliography
7
Chapter 5: Addressing Bias and Ethical Concerns in LLM-Generated Code
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Chapter 5: Addressing Bias and Ethical Concerns in LLM-Generated Code
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Technical requirements
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Understanding bias in LLM-generated code
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Examining ethical dilemmas – challenges in LLM-enhanced working
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Detecting bias – tools and strategies
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Preventing biased code – coding with ethical considerations
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Summary
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Bibliography
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Chapter 6: Navigating the Legal Landscape of LLM-Generated Code
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Chapter 6: Navigating the Legal Landscape of LLM-Generated Code
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Technical requirements
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Unraveling copyright and intellectual property considerations
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Addressing liability and responsibility for LLM-generated code
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Examining legal frameworks governing the use of LLMs in coding
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Possible future of the regulation of AI-generated code
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Summary
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Bibliography
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Chapter 7: Security Considerations and Measures
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Chapter 7: Security Considerations and Measures
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Technical requirements
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Understanding the security risks of LLMs
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Implementing security measures for LLM-powered coding
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Making the future more secure
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Summary
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Bibliography
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Part 3: Explainability, Shareability, and the Future of LLM-Powered Coding
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Part 3: Explainability, Shareability, and the Future of LLM-Powered Coding
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Chapter 8: Limitations of Coding with LLMs
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Chapter 8: Limitations of Coding with LLMs
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Technical requirements
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Inherent limitations of LLMs
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Challenges in integrating LLMs into coding workflows
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Future research directions to address limitations
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Summary
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Bibliography
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Chapter 9: Cultivating Collaboration in LLM-Enhanced Coding
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Chapter 9: Cultivating Collaboration in LLM-Enhanced Coding
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Technical requirements
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Why share LLM-generated code?
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Best practices for code sharing
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Knowledge management – capturing and sharing expertise
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Making the best use of collaborative platforms
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Summary
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Bibliography
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Chapter 10: Expanding the LLM Toolkit for Coders: Beyond LLMs
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Chapter 10: Expanding the LLM Toolkit for Coders: Beyond LLMs
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Technical requirements
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Code completion and generation tools
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SCA and code review tools
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Testing and debugging tools
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Summary
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Bibliography
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Part 4: Maximizing Your Potential with LLMs: Beyond the Basics
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Part 4: Maximizing Your Potential with LLMs: Beyond the Basics
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Chapter 11: Helping Others and Maximizing Your Career with LLMs
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Chapter 11: Helping Others and Maximizing Your Career with LLMs
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Why Mentor Others in LLM-powered coding?
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Other Ways to Share Your Expertise and Work
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Attend, Build, Network
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New Approaches from LLMs
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Summary
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Bibliography
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Chapter 12: The Future of LLMs in Software Development
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Chapter 12: The Future of LLMs in Software Development
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Technical requirements
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Emerging trends in LLM technologies
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Future impacts
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Coming challenges and opportunities
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Summary
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Bibliography
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Index
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Index
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Why subscribe?
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Other Books You May Enjoy
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Other Books You May Enjoy
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Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and X handles. Here is an example: “Next, we have Prompt 5 as a Flask app (app.py) with Python code.”

A block of code is set as follows:

<!DOCTYPE html> 
<html> 
<head> 
<title>Button Click</title> 
<script> 
function sayHello() { 
  alert("Hello!"); 
}
</script> 
</head> 
<body> 
<button onclick="sayHello()">Click me</button> 
</body> 
</html>

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import pandas as pd 
import matplotlib.pyplot as plt 
# Sample data (replace with your data) 
data = pd.Series([1, 2, 3, 4, 5]) 
# Assuming the data is in a column named "values" 
fig, ax = plt.subplots() 
ax.plot(data) 
ax.set_xlabel("Index") 
ax.set_ylabel("Value") 
ax.set_title("Line Plot of Data") 
plt.show()

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “You'd have to click the first button, Click me, to get the pop-up window again.”

Tips or important notes

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