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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
Languages
Concepts
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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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Toc

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

Challenges in integrating LLMs into coding workflows

First, we should look at what the challenges are with LLM-generated code and workflows. Here are some of them:

  • Code quality and reliability, security risks, and dependency management have already been mentioned.
  • Explainability: Understanding the logic behind LLM-generated code can be difficult, making debugging and maintenance challenging
  • Contextual understanding: The LLM understands a small context but not the whole code base or project, so its use may lead to incompatibility with the other code or not generate code that’s in the same style
  • Code snippet length: LLMs may struggle to understand and process long code snippets, leading to incomplete or inaccurate responses
  • Specialized domains: LLMs trained on general-purpose datasets might lack the deep domain-specific knowledge required for certain coding tasks, such as medical imaging or financial modeling
  • Fixing complex errors: While good at finding...
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