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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

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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1
Part 1: Introduction to LLMs and Their Applications
5
Part 2: Be Wary of the Dark Side of LLM-Powered Coding
10
Part 3: Explainability, Shareability, and the Future of LLM-Powered Coding
14
Part 4: Maximizing Your Potential with LLMs: Beyond the Basics

Making it work for you

Here are some tips to make coding with LLMs work better for you:

  • Be specific about what you want the code to do and look like.
  • Master prompt engineering. Craft your prompts meticulously. The clearer and more detailed your instructions, the better the LLM understands your intent and generates relevant code. Think of prompts as precise questions guiding the LLM toward creating your desired solution.
  • Tell the LLM which version of which language you’re using and which IDE/environment you’re using for the code. For example, I have been using Python 3.10 in a Spyder IDE.
  • Break down tasks into smaller chunks. Don’t overwhelm the LLM with complex, multi-step tasks. Instead, break them down into smaller, manageable subtasks. This allows for more focused prompts and facilitates easier troubleshooting and refinement.
  • Go through iterations, improving all the time. Use the LLM to check how you can improve, and get it to ask you...

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