AI-powered applications are everywhere, but many tutorials focus only on connecting an API and displaying a response.
I wanted to build something closer to a real-world application.
So I created a modern AI Assistant using vanilla HTML, CSS, and JavaScript with features such as streaming responses, markdown rendering, syntax highlighting, chat history, theme switching, and deployment.
In this article, I'll walk through the architecture, key features, lessons learned, and common mistakes to avoid.
The Final Result
The application includes:
✅ Modern responsive UI
✅ Dark and Light Mode
✅ Streaming AI Responses
✅ Markdown Rendering
✅ Syntax Highlighting
✅ Chat History Persistence
✅ Copy-to-Clipboard Support
✅ Error Handling and Retry Logic
✅ GitHub Pages Deployment
Unlike many beginner projects, this one focuses on both user experience and maintainability.
Why I Built This Project
Many AI chatbot tutorials stop after displaying a response from the API.
However, real applications need much more:
- Better user experience
- Error handling
- State management
- Responsive design
- Safe content rendering
- Persistent conversations
My goal was to build something that developers could use as a portfolio project while learning practical frontend development concepts.
Tech Stack
The project uses:
Frontend
- HTML5
- CSS3
- Vanilla JavaScript
Libraries
- Marked.js
- DOMPurify
- Prism.js
AI Integration
- Google Generative AI SDK
Deployment
- GitHub Pages
No frameworks.
No build tools.
Just modern JavaScript.
One Mistake Most AI Tutorials Make
One of the biggest issues I see in AI tutorials is rendering AI-generated content directly into the page.
This can create security risks.
Instead, I used DOMPurify to sanitize AI responses before displaying them.
Example:
const cleanHtml = DOMPurify.sanitize(rawHtml);
A small addition can significantly improve security.
Streaming Responses Improve User Experience
Rather than waiting for the entire response, the application streams content as it is generated.
This creates a smoother and more natural chat experience.
Users receive feedback immediately instead of staring at a loading indicator.
Making Code Responses Look Better
Many AI assistants generate code snippets.
To improve readability, I integrated Prism.js for syntax highlighting and added a custom code block header with:
- Language labels
- Copy buttons
- Better formatting
This dramatically improves the experience for developers using the application.
Chat History Persistence
Users expect conversations to remain available after refreshing the page.
To achieve this, I used localStorage.
Benefits:
- Faster user experience
- No backend required
- Simple implementation
This also makes the project ideal for deployment on GitHub Pages.
Error Handling Matters
Most demos work perfectly during development.
Real applications don't.
I added:
- Retry Logic
- API Error Handling
- User-Friendly Messages
- Loading States
These features make the application more resilient and production-ready.
Deployment in Minutes
Once the project was complete, deployment was surprisingly simple.
GitHub Pages allowed me to host the entire application for free.
This makes the project accessible to anyone who wants to showcase it in a portfolio or share it publicly.
What I Learned Building This Project
Building an AI application is about much more than calling an API.
The most valuable lessons came from:
- Handling edge cases
- Improving UX
- Managing state
- Securing AI output
- Optimizing frontend performance
These areas often have a greater impact on users than the AI model itself.
Full Step-by-Step Video Tutorial
In the complete tutorial, I cover:
- Gemini API Setup
- Project Structure
- UI Design
- JavaScript Logic
- Streaming Responses
- Syntax Highlighting
- Error Handling
- Deployment to GitHub Pages
Watch the full build here: 👉 [https://youtu.be/nbFKG_6DkSE]
Final Thoughts
AI projects are a fantastic way to improve frontend development skills because they combine:
- APIs
- UI Design
- State Management
- User Experience
- Real-World Problem Solving
If you're building a developer portfolio in 2025, creating projects like this can help you learn practical skills while producing something users genuinely enjoy using.
I'd love to hear what feature you would add next.
Voice Input?
File Uploads?
Memory?
Let me know in the comments.
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