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Coding with ChatGPT and Other LLMs

Coding with ChatGPT and Other LLMs

By : Dr. Vincent Austin Hall
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Coding with ChatGPT and Other LLMs

Coding with ChatGPT and Other LLMs

4 (4)
By: Dr. Vincent Austin Hall

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 Gemini 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

Implementing security measures for LLM-powered coding

As we integrate LLMs into our development workflows, it’s crucial to implement robust security measures. These measures will help ensure that our LLM-assisted code is ready for real-world deployment. Let’s explore key areas of focus and practical steps to enhance security in LLM-powered coding environments.

Here are seven measures that should be taken to get more secure code.

Input sanitization and validation

When using LLMs for code generation or completion, it’s important to sanitize and validate all inputs, both those provided to the LLM and those generated by it.

Validation is where the data is checked to make sure it’s correct/accurate before processing or using it. Sanitization is where the data is cleaned, where parts that could be dangerous are removed or changed enough that they’re not dangerous [NinjaOne, Informatica].

Before passing any input to an LLM, validate it against...

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Coding with ChatGPT and Other LLMs
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