This content originally appeared on DEV Community and was authored by horus he
Last week, I watched our analytics dashboard in horror. 87% of users were abandoning our AI jersey designer during the generation process. The culprit? A spinning loader that lasted 15-20 seconds with zero feedback.
Sound familiar? If you’re building AI features, you’ve probably faced this exact problem. Here’s how I transformed those painful wait times into a smooth, engaging experience that actually keeps users around.
The $10,000 Problem
Our AI jersey generator was bleeding users and money. Every abandoned generation meant:
- Wasted AI compute costs ($0.04 per failed attempt)
- Lost conversion opportunity ($12 average order value)
- Negative brand perception (users thought the app was broken)
After losing nearly $10,000 in potential revenue in just one month, I knew we needed a radical rethink.
The Magic: Async Processing + Smart Polling
Instead of making users wait, I split the process into three phases:
// 1. Instant submission - returns in 200ms
async function submitDesign(request: Request) {
const validation = validateInput(request.body);
if (!validation.success) return { error: validation.error };
// Create async task and return immediately
const predictionId = await createPrediction({
prompt: request.body.prompt,
webhookUrl: `${API_URL}/webhooks/ai-complete`
});
// Store initial status
await kvStore.put(`prediction:${predictionId}`, {
status: 'starting',
createdAt: Date.now()
});
return {
predictionId,
message: 'Your design is being created!'
};
}
The Frontend Magic
Here’s where it gets interesting. Instead of a boring spinner, users see real progress:
function JerseyGenerator() {
const [status, setStatus] = useState('idle');
const [progress, setProgress] = useState(0);
async function pollStatus(predictionId: string) {
const delays = [1000, 2000, 5000, 10000]; // Progressive delays
let attempt = 0;
while (attempt < 60) {
const result = await fetch(`/api/status/${predictionId}`);
const data = await result.json();
if (data.status === 'processing') {
setProgress(Math.min(attempt * 10, 90)); // Visual progress
setStatus('AI is crafting your unique design...');
} else if (data.status === 'succeeded') {
setProgress(100);
displayResult(data.imageUrl);
return;
}
const delay = delays[Math.min(attempt, delays.length - 1)];
await sleep(delay);
attempt++;
}
}
return (
<div>
{status !== 'idle' && (
<ProgressBar value={progress} message={status} />
)}
</div>
);
}
The Webhook Secret Sauce
When the AI completes, a webhook instantly updates the status:
async function handleWebhook(request: Request) {
const event = await request.json();
// Verify webhook signature (crucial for security!)
if (!verifySignature(request)) {
return new Response('Unauthorized', { status: 401 });
}
if (event.status === 'succeeded') {
// Download and store the result
const imageUrl = await storeImage(event.output[0]);
// Update status for frontend polling
await kvStore.put(`prediction:${event.id}`, {
status: 'succeeded',
imageUrl,
completedAt: Date.now()
});
}
return new Response('OK');
}
Real Production Results
After implementing this architecture at AI Jersey Design:
User Engagement:
- Abandonment rate: 87% → 12%
- Average session duration: +340%
- Conversion rate: 2.3% → 8.7%
Performance:
- Initial response: 200ms (was 15+ seconds)
- P95 completion time: 8 seconds
- Successful generations: 99.2%
Business Impact:
- Revenue increase: +278%
- Support tickets: -65%
- AI cost per conversion: -40%
The Gotchas Nobody Talks About
Webhook Retries: AI services retry failed webhooks. Without idempotency, you’ll process duplicates.
Status Expiration: Set TTLs on your KV storage. I learned this after accumulating 100GB of orphaned predictions.
Progressive Delays: Don’t poll every second! Use exponential backoff to save bandwidth.
Error Recovery: When webhooks fail, have a backup polling mechanism to check AI service directly.
Quick Implementation Checklist
If you’re implementing this pattern, here’s your checklist:
- [ ] Non-blocking API endpoint that returns immediately
- [ ] KV storage for status with automatic TTL
- [ ] Webhook endpoint with signature verification
- [ ] Frontend polling with progressive delays
- [ ] Progress indicators beyond just spinners
- [ ] Error handling for each failure mode
- [ ] Monitoring for webhook delivery rates
The Architecture That Scales
This pattern has handled:
- Peak load: 500+ concurrent generations
- Daily volume: 10,000+ images
- Global users: <50ms status checks worldwide
- Zero downtime: During 3 months of production
Your Turn
What’s your approach to handling long-running tasks? Have you tried async patterns in your AI apps? I’d love to hear what worked (or didn’t) for you.
Drop a comment with your experience, or share your horror stories of users abandoning your AI features. Let’s solve this together!
Found this helpful? Follow me for more real-world AI architecture patterns. Next week: How I cut our AI costs by 73% without sacrificing quality.
This content originally appeared on DEV Community and was authored by horus he