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Real-Time AI: Making Slow Models Feel Fast

Perceived performance matters more than actual performance.

AI, Real-Time•Sep 5, 2024•4 min read

Large language models are slow. GPT-4 can take 10-20 seconds to generate a full response. That's an eternity in user experience terms. People will give up, assume it's broken, or lose trust in your product.

But here's the thing: you don't need faster models. You need better user experience. Streaming, progressive rendering, and smart architecture can make a 15-second response feel like a 1-second response.

Why perception beats reality

A loading spinner for 10 seconds feels longer than watching text appear over 12 seconds. Counterintuitive, but consistently true in user research.

The difference is feedback. When text streams in, users:

  • •Know the system is working (not frozen or crashed).
  • •Can start reading before generation finishes.
  • •Can interrupt if it's going in the wrong direction.
  • •Feel like they're in a conversation, not submitting a batch job.

This is why ChatGPT streams responses by default. It's not a technical necessity - it's a UX decision that transforms perceived performance.

Streaming: the minimum viable improvement

Every major LLM API supports streaming. Instead of waiting for the full response, you get tokens as they're generated. Implementing this is straightforward - most SDKs handle it out of the box.

The client-side work is where complexity lives:

  • •Connection handling: HTTP connections can drop. Your UI needs to handle reconnection gracefully, not just show an error.
  • •Partial content rendering: Markdown halfway through a code block looks broken. You need to handle incomplete structures.
  • •User interactions during streaming: What happens if they click "stop" or navigate away? State management gets interesting.
  • •Error states: If generation fails at token 500 of 1000, do you show what you got or discard it?

Use Server-Sent Events (SSE) for web. WebSockets are overkill unless you need bidirectional communication. SSE is simpler, handles reconnection better, and works through more proxies.

The tool call problem

Streaming works great for pure text generation. It breaks down when AI needs to call tools.

Typical flow: Model decides to call a tool. Generation pauses. Your backend executes the tool. Results go back to the model. Generation continues. The user sees... nothing. For potentially several seconds.

Better approaches:

  • •Show tool call status: "Searching your documents..." gives users context. They're waiting for something specific, not just waiting.
  • •Progressive tool results: If a tool returns multiple items, stream them as they're retrieved.
  • •Parallel execution: When the model wants to call multiple tools, execute them simultaneously.
  • •Optimistic UI: Show "retrieving data" cards immediately, fill them in as results arrive.

Don't hide tool calls. Users should see the AI is "thinking" differently than when it's "typing." Opacity breeds distrust.

Architecture that enables speed

Beyond streaming, there's infrastructure work that compounds the improvements.

  • •Edge deployment: Run your AI orchestration layer at the edge. Cuts round-trip latency for the initial connection.
  • •Smart caching: Not all requests need fresh generation. Cache common queries, especially for RAG where the underlying docs haven't changed.
  • •Model routing: Use faster models for simple queries, slower models for complex ones. Not every question needs GPT-4.
  • •Warm connections: Keep connections to LLM APIs warm. Cold starts add hundreds of milliseconds.

None of this is revolutionary. It's standard web performance engineering applied to AI. The difference is AI has much worse baseline latency, so optimizations have outsized impact.

When this matters (and when it doesn't)

Real-time optimization is essential for:

  • •User-facing chat interfaces. People expect conversational response times.
  • •Inline AI features (autocomplete, suggestions). Speed is the entire value proposition.
  • •Mobile apps. Attention spans are shorter, network conditions are worse.
  • •Collaborative tools. When multiple people are watching, delays feel even longer.

It's not worth the effort for:

  • •Background processing. If users aren't watching, who cares how fast it streams?
  • •Email-style async delivery. Users already expect to wait.
  • •Short responses. If generation takes 2 seconds total, streaming adds complexity for little benefit.

Voice AI has even stricter requirements. 200ms response latency is the threshold for natural conversation. That's a different engineering challenge entirely.

Make your AI feel instant

We implement streaming, optimize architecture, and build the infrastructure that transforms sluggish AI into responsive experiences. From SSE pipelines to edge deployment, we handle the engineering so your users stop waiting.

Book a call→

or email partner@greenfieldlabsai.com

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