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Duration 14 hours
Course Outline
Introduction to Generative AI in Front-End
- Defining generative AI within the context of software development.
- An overview of key tools such as ChatGPT, GitHub Copilot, and Codeium.
- Understanding the advantages and constraints of AI in UI development.
Generating UIs via Prompts
- Formulating prompts to create HTML structures and components.
- Creating and adjusting CSS styles with the assistance of AI.
- Utilizing AI to scaffold interactive JavaScript elements.
Layout Prototyping with Generative Tools
- Constructing landing pages and complex multi-section layouts.
- Applying responsive design prompts for Flexbox and Grid systems.
- Previewing and testing results using platforms like CodePen or similar.
Componentization and Reusability
- Generating reusable UI components such as buttons, cards, and forms.
- Building component libraries and design systems with AI assistance.
- Integrating AI into popular frameworks like React, Vue, and Tailwind.
AI-Assisted Code Review and Debugging
- Resolving layout bugs and accessibility concerns using LLMs.
- Enhancing the performance of HTML, CSS, and JS code.
- Interpreting errors and proposing solutions through AI prompts.
Collaborative Design and Content Generation
- Using AI to create placeholder content, copy, and mockups.
- Collaborating with designers to co-create wireframes and styling.
- Translating AI-generated concepts into functional HTML templates.
Project: Constructing an AI-Scaffolded Web App
- Designing the UI guided by specific business prompts.
- Developing components and interactions with AI support.
- Refining, testing, and presenting the final prototype.
Wrap-up and Future Directions
Requirements
- Foundational knowledge of HTML, CSS, and JavaScript
- Awareness of front-end frameworks or established design systems
- A desire to integrate AI into UI/UX processes to improve speed
Target Audience
- Front-end developers
- UX engineers
- Web designers and creative technologists
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
