Production-Ready AI Interfaces with Next.js

Building AI interfaces that feel polished, accessible, and production-ready is harder than it looks. Most teams start with a basic textarea and end up rebuilding keyboard navigation, streaming responses, feedback UIs, and accessibility patterns from scratch—wasting weeks that could be spent on your core product.

The AI UX Kit gives you production-ready React & Next.js components for prompt flows, streaming responses, feedback loops, and AI configuration panels—all built with WCAG AA accessibility and token-driven theming.

🎯 What Makes an AI Interface Production-Ready?

Before diving into code, let's define what "production-ready" actually means for AI interfaces:

1. Accessible by default: Keyboard navigation, screen reader support, proper ARIA labels
2. Streaming-ready: Handle real-time token streaming without UI jank
3. Feedback loops: Capture user ratings and comments to improve your model
4. Token-driven: Design tokens synced between Figma and Tailwind for consistent theming
5. Mobile-responsive: Works beautifully on all screen sizes

💻 Building a Prompt Input Component

The prompt input is the foundation of any AI interface. Here's how the AI UX Kit implements a production-ready prompt input:

import { PromptInput } from '@/components/ui/composites/prompt-input'

function ChatInterface() {
  const [prompt, setPrompt] = React.useState('')
  const [files, setFiles] = React.useState([])

  const handleSubmit = async (value: string, attachedFiles: AttachedFile[]) => {
    // Send to your AI API
    await sendToAI(value, attachedFiles)
  }

  return (
    <PromptInput
      value={prompt}
      onChange={setPrompt}
      onSubmit={handleSubmit}
      placeholder="Ask anything..."
      suggestions={[
        { id: '1', text: 'Summarize this document', category: 'actions' },
        { id: '2', text: 'Explain in simple terms', category: 'actions' },
      ]}
      attachedFiles={files}
      onFileAttach={(newFiles) => setFiles([...files, ...newFiles])}
      actionButton="send"
      showSuggestions={true}
    />
  )
}

Key Features

• Auto-resizing textarea that grows with content
• Keyboard navigation for suggestions (Arrow keys, Enter, Escape)
• File attachments with preview and removal
• Voice input support with recording states
• Accessible focus management for screen readers

🔄 Handling Streaming Responses

Streaming AI responses require careful UX considerations. The component handles live regions for screen readers and smooth rendering:

import { ResponseViewer } from '@/components/ui/composites/response-viewer'

function StreamingResponse({ stream }: { stream: ReadableStream }) {
  const [content, setContent] = React.useState('')
  
  React.useEffect(() => {
    const reader = stream.getReader()
    const decoder = new TextDecoder()
    
    async function readStream() {
      while (true) {
        const { done, value } = await reader.read()
        if (done) break
        
        const chunk = decoder.decode(value)
        setContent(prev => prev + chunk)
      }
    }
    
    readStream()
  }, [stream])

  return (
    <ResponseViewer
      content={content}
      format="markdown"
      showCitations={true}
      citations={extractCitations(content)}
      aria-live="polite"
      aria-atomic="false"
    />
  )
}

The aria-live="polite" attribute ensures screen readers announce updates without interrupting the user, while the component handles markdown rendering, code syntax highlighting, and citation links.

⭐ Implementing Feedback Loops

Capturing user feedback is crucial for improving your AI model. Here's how to integrate the feedback modal:

import { FeedbackModal } from '@/components/ui/templates/modal/feedback-modal'

function AIResponseWithFeedback({ responseId }: { responseId: string }) {
  const [showFeedback, setShowFeedback] = React.useState(false)

  const handleFeedbackSubmit = async (data: { rating: number; feedback: string }) => {
    await fetch('/api/feedback', {
      method: 'POST',
      body: JSON.stringify({
        responseId,
        rating: data.rating,
        feedback: data.feedback,
      }),
    })
    setShowFeedback(false)
  }

  return (
    <>
      <div className="flex items-center gap-2">
        <ResponseViewer content={response} />
        <button
          onClick={() => setShowFeedback(true)}
          className="text-sm text-secondary-600 hover:text-primary-600"
        >
          Rate this response
        </button>
      </div>

      <FeedbackModal
        isOpen={showFeedback}
        onClose={() => setShowFeedback(false)}
        onSubmit={handleFeedbackSubmit}
      />
    </>
  )
}

The feedback modal includes:

• 5-star rating system with keyboard navigation
• Optional text feedback field
• Accessible form validation
• Loading states during submission

🎨 Token-Driven Theming

Design tokens keep your UI consistent across light and dark modes. The AI UX Kit uses Tailwind with CSS custom properties:

// tailwind.config.js
module.exports = {
  theme: {
    extend: {
      colors: {
        primary: {
          50: 'var(--color-primary-50)',
          100: 'var(--color-primary-100)',
          // ... mapped from Figma variables
          600: 'var(--color-primary-600)',
        },
      },
      spacing: {
        // Tokenized spacing scale
        'token-1': 'var(--spacing-token-1)',
        'token-2': 'var(--spacing-token-2)',
      },
    },
  },
}

This ensures your Figma designs translate directly to code, and theme changes propagate automatically.

📱 Complete Prompt Screen Example

Here's a full example combining all components into a production-ready prompt screen:

import { PromptScreen } from '@/components/ui/recipes/prompt-submission/prompt-screen'

export default function HomePage() {
  return (
    <PromptScreen
      heading="What can I help you with?"
      placeholder="Ask anything..."
      suggestedActions={[
        { id: '1', text: 'Adjust lighting', icon: 'SunIcon' },
        { id: '2', text: 'Remove Background', icon: 'PhotoIcon' },
        { id: '3', text: 'Improve image quality', icon: 'SparklesIcon' },
      ]}
      onPromptSubmit={(prompt) => {
        router.push(`/chat?prompt=${encodeURIComponent(prompt)}`)
      }}
    />
  )
}

This single component provides:

• Centered prompt input with suggestions
• Topbar navigation
• Footer with terms link
• Mobile-responsive layout
• Accessible keyboard flows

✅ Production Checklist

Before shipping your AI interface, ensure:

• Keyboard navigation works for all interactive elements
• Screen reader announces streaming responses appropriately
• Focus management handles modals and dynamic content
• Error states are clear and actionable
• Loading states provide feedback during API calls
• Mobile experience is polished and touch-friendly
• Design tokens are synced between design and code

🚀 Get Started Faster

Building these patterns from scratch takes weeks. The AI UX Kit gives you:

• ✅ Pre-built components for prompt flows, streaming, and feedback
• ✅ WCAG AA accessibility built-in
• ✅ Token system synced with Figma
• ✅ Next.js + Tailwind codebase ready to customize
• ✅ Production-tested patterns from real AI products

Explore the AI UX Kit: View components

❓ FAQ

Do I need to use Next.js?

The components are built for Next.js 14+ with App Router, but the patterns can be adapted to other React frameworks.

How do I wire real AI streaming?

Use your LLM SDK (like Vercel AI SDK) and pass the stream to the ResponseViewer component. The kit includes integration examples.

What about backend integration?

The kit focuses on frontend UX. You'll wire your own API endpoints for AI calls, authentication, and data persistence.

Can I customize the styling?

Yes! All components use Tailwind classes and design tokens, making customization straightforward while maintaining consistency.