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Coding with ChatGPT and Other LLMs

You're reading from   Coding with ChatGPT and Other LLMs Navigate LLMs for effective coding, debugging, and AI-driven development

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781805125051
Length 304 pages
Edition 1st Edition
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Authors (2):
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Dr. Vincent Austin Hall Dr. Vincent Austin Hall
Author Profile Icon Dr. Vincent Austin Hall
Dr. Vincent Austin Hall
Chigbo Uzokwelu Chigbo Uzokwelu
Author Profile Icon Chigbo Uzokwelu
Chigbo Uzokwelu
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Table of Contents (19) Chapters Close

Preface 1. Part 1: Introduction to LLMs and Their Applications
2. Chapter 1: What is ChatGPT and What are LLMs? FREE CHAPTER 3. Chapter 2: Unleashing the Power of LLMs for Coding: A Paradigm Shift 4. Chapter 3: Code Refactoring, Debugging, and Optimization: A Practical Guide 5. Part 2: Be Wary of the Dark Side of LLM-Powered Coding
6. Chapter 4: Demystifying Generated Code for Readability 7. Chapter 5: Addressing Bias and Ethical Concerns in LLM-Generated Code 8. Chapter 6: Navigating the Legal Landscape of LLM-Generated Code 9. Chapter 7: Security Considerations and Measures 10. Part 3: Explainability, Shareability, and the Future of LLM-Powered Coding
11. Chapter 8: Limitations of Coding with LLMs 12. Chapter 9: Cultivating Collaboration in LLM-Enhanced Coding 13. Chapter 10: Expanding the LLM Toolkit for Coders: Beyond LLMs 14. Part 4: Maximizing Your Potential with LLMs: Beyond the Basics
15. Chapter 11: Helping Others and Maximizing Your Career with LLMs 16. Chapter 12: The Future of LLMs in Software Development 17. Index 18. Other Books You May Enjoy

Future impacts

The increasing capabilities of LLMs will fundamentally change the landscape of software development. One of the most significant impacts will be the democratization of coding. As LLMs make it easier for non-programmers to write functional code, they will enable more people to contribute to software development without needing deep expertise in programming languages. This shift could lead to a proliferation of new apps and tools created by diverse individuals and small teams.

LLMs will also drive significant advances in automated software testing. With models that can understand and generate test cases, software reliability is likely to increase, as AI-driven systems will help identify bugs and issues before code is ever deployed.

The role of developers is likely to change as well. Instead of focusing primarily on writing code, many developers will shift toward problem-solving, code optimization, and creative ideation, leaving routine tasks to AI assistants. As...

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