You are building a web application with GitHub Copilot and need to write a function to handle user authentication, ensuring it is secure. You notice that when you give a simple prompt like “write an authentication function,” Copilot generates a solution that uses outdated practices, such as storing plain-text passwords. You realize you need to refine your prompt to ensure Copilot suggests secure, up-to-date practices. Which of the following would be the best prompt to help GitHub Copilot generate a secure authentication function?
You are a software developer working on a large codebase. Your company has implemented GitHub Copilot to help developers be more productive by providing AI-based code suggestions. You’re currently working on a new feature in an existing project, and you need to implement a complex function. Copilot suggests a code block that appears to do what you need, but it is unfamiliar to you. Which of the following is the best practice for using Copilot in this scenario to ensure both productivity and code quality?
You are a developer who is concerned about how GitHub Copilot processes and stores your data, especially when you are working on sensitive projects. Your company handles confidential data, and you want to ensure that no private code or data is being shared or stored in ways that could compromise security. Which statement best describes how GitHub Copilot handles user data, including your code and suggestions?
You are working on a large project and notice that GitHub Copilot’s suggestions sometimes seem irrelevant to the code you are working on, especially when dealing with files with many lines of code. You want to understand why this is happening and how GitHub Copilot handles large files. Which of the following statements best describes the limitations of GitHub Copilot in relation to limited context windows?
You are developing an AI-based recruiting system and are using GitHub Copilot to help write code that filters job applicants based on their qualifications. Given that Copilot’s training data might contain historical biases (e.g., gender, race), how can you ensure that the code it generates does not inadvertently introduce bias into the system?