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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

Summary

In this chapter, we covered what ChatGPT is and what LLMs in general are, the origins of some widely used LLMs such as BERT, the GPT family, LlaMDA, LlaMA, and modern LLMs such as GPT-4 and Gemini. We looked at some architecture of LLMs and transformers. We had a go at fully processing a sentence in the way an LLM model would: tokenizing, Word2Vec contextual embedding, and more. We also touched on the types of mathematics involved and the applications of this fantastic technology deployed by companies.

Hopefully, you now understand the nature of LLMs such as ChatGPT/Gemini; understand the architectures of LLMs; understand some mathematics of LLMs; and are enlightened about competition in the field and how to teach LLMs to others.

In Chapter 2, we will look at the advantages of coding with LLMs, planning your LLM-powered coding, doing some coding with LLMs, and making it work for you.

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