AI-Powered Software Engineering

Explain the fundamental concepts of version control with Git (repository, commit, branch, remote).

Initialize a new Git repository for a project and perform the first commit.

Use an AI assistant to generate clear and conventional Git commit messages.

Practice the basic Git workflow: staging changes, committing, and viewing project history.

Create and switch between branches to manage different lines of development.

Use an AI assistant to explain and convert legacy JavaScript to modern ES6+ syntax (e.g., arrow functions, const/let, destructuring).

Generate and explain asynchronous JavaScript operations using Promises and async/await with AI.

Refactor legacy procedural code into modern, modular JavaScript functions using AI.

Debug JavaScript runtime errors by providing stack traces and code snippets to an LLM for analysis.

Write effective natural language prompts to generate boilerplate code for common JavaScript tasks.

Use an AI assistant to explain the roles of HTML, CSS, and the DOM in creating web pages.

Prompt an AI to generate the semantic HTML5 structure for a single webpage component (e.g., a navigation bar).

Explain the core principles of responsive design and the CSS box model with help from an AI assistant.

Use an AI to generate CSS for styling a component, including colors, fonts, and spacing.

Prompt an AI to apply a responsive layout to a component using CSS Flexbox.

Use an AI assistant to explain the core concepts of React: components, JSX, props, and state.

Scaffold a new React application using a modern build tool like Vite.

Prompt an AI to create a basic React functional component that manages its own internal state (e.g., a counter).

Decompose a static HTML/CSS component into a reusable React component that accepts props.

Use an AI assistant to apply styles to React components using a utility-first framework like Tailwind CSS.

Prompt an AI to scaffold a basic Node.js backend server using the Express framework.

Generate REST API endpoints for standard CRUD (Create, Read, Update, Delete) operations using an AI assistant.

Use an AI tool to draft database connection logic for a NoSQL database like MongoDB.

Prompt an AI to write and explain server-side data validation and error handling logic.

Generate client-side API fetching logic inside a React component to consume data from the backend.

Use an AI to troubleshoot and resolve cross-origin resource sharing (CORS) issues between frontend and backend.

Set up a React Native development environment using Expo with AI-guided instructions.

Use an AI assistant to identify fundamental React Native components (e.g., View, Text) and their web equivalents.

Prompt an AI to translate a web-based React component into a React Native mobile component.

Apply Flexbox for creating responsive UI layouts in the React Native environment.

Use AI tools to implement basic stack navigation between different screens in a mobile app.

Use an AI assistant to explain the purpose of unit testing and the Arrange-Act-Assert pattern.

Prompt an AI assistant to generate a unit test for a pure JavaScript function.

Generate a unit test for a React component to verify its rendering and behavior.

Generate a test case for a backend API endpoint to validate its response.

Use an LLM to brainstorm and define the core features and user stories for a personal capstone project.

Prompt an LLM to design a basic data model (schema) for the application's database based on the defined features.

Outline the necessary REST API endpoints required to support the application's features, using an LLM for suggestions.

Sketch a high-level component tree for the frontend application with the help of an LLM.

Implement the logic for fetching data from a backend API and displaying it in a frontend component using an AI assistant.

Prompt an AI tool to generate code for handling loading and error states during API communication in the frontend.

Use AI to create a form in the frontend that submits data to a backend API endpoint to create a new resource.

Integrate the frontend and backend components to build a complete application feature.

Use an AI assistant to compare beginner-friendly deployment platforms (e.g., Vercel, Render) for a full-stack application.

Prompt an AI tool to explain and generate the necessary environment variable configurations for production.

Deploy the frontend portion of the application to a cloud platform via Git integration.

Deploy the backend API portion of the application to a cloud platform and connect it to a production database.

Use AI to verify the successful deployment and connectivity between the live frontend and backend services.