Working on a chatbot interface using OpenAI's API and struggling with the UX during those inevitable 3-8 second response delays. Currently showing a basic spinner, but it feels inadequate for longer waits.
I've experimented with a few approaches:
Streaming responses - Using SSE to show tokens as they generate. Works great for longer responses but feels janky for short ones where the stream ends almost immediately.
Progressive skeleton states - Show message bubble → typing indicator → partial content placeholder. Reduces perceived latency but adds complexity.
Activity indicators with context - "Thinking..." → "Almost there..." with progress hints. Users seem to tolerate longer waits when they know what's happening.
The streaming approach cut perceived wait time significantly, but I'm seeing 15-20% more users abandon mid-generation compared to atomic responses. Maybe the unpredictable timing creates anxiety?
Anyone found good patterns for managing LLM latency expectations? Particularly interested in how you handle the transition from loading state to content rendering. Using React with Tanstack Query for state management.
Considering implementing adaptive loading states based on estimated response length, but worried about over-engineering. What's worked for your AI interfaces?
Been wrestling with this exact issue at our startup. Streaming looks slick but adds complexity - more API calls, WebSocket maintenance, error handling edge cases. We're bootstrapped so dev time = money. Ended up with a hybrid: skeleton UI for first 2s, then chunked loading indicators with estimated progress. Uses same single API call but feels more responsive. Cost per response stayed flat while perceived performance improved 40% in user tests. Sometimes the boring solution wins.
I shipped this problem 3 times now. Here's what actually works: combine skeleton states with micro-interactions. Show typing dots for 0-2s, skeleton UI for 2-5s, then add a subtle progress hint after 5s. The key insight? Users tolerate longer waits when they see something happening. Also cache common responses aggressively - probably 30% of your queries are variations of the same thing. Oh, and add an escape hatch button after 8s. Trust me on that one.
Working on similar patterns for Hugging Face Transformers.js. The real issue isn't just UX - it's handling partial failures in streams. Most devs forget about connection drops mid-response. We're experimenting with resumable streaming using checkpoint tokens. Also consider model size vs latency tradeoffs - sometimes a smaller, faster model with better UX beats a slower accurate one. Check our new streaming examples in v2.1.7, specifically the progressive enhancement pattern.
This might be a dumb question, but how do you actually implement the skeleton states? I get the concept but struggling with the technical part. Do you pre-render different skeleton layouts based on expected response types? And for streaming - is that just chunking the API response or do you need WebSockets? Also, what happens if the stream breaks halfway through? Sorry, still learning this stuff but my users are definitely noticing the delays.
Red flag on streaming implementations - seen too many devs skip input validation on partial responses. Each streamed chunk should be sanitized before rendering, especially if users can inject prompts. Also consider rate limiting per connection, not just per user. WebSocket streams bypass traditional request throttling. And log everything - truncated streams often hide injection attempts. The UX improvement isn't worth it if you're leaking data or getting XSS'd through partial renders.
