AI models have become incredibly fast. Network latency has improved. Yet many AI chat apps still feel slow. This isn’t a hardware problem or a model problem.
It’s a user experience problem.
The Real Problem: AI Chat Apps Feel Slow
When a user sends a message and the UI stays blank even briefly the brain interprets that silence as delay.
From the user’s perspective:
- Did my message go through?
- Is the app frozen?
- Is the model slow?
In most cases, none of this is true.
But perception matters more than reality.
Latency in AI apps is psychological before it is technical.
Why Waiting for the Full Response Breaks UX
Many AI chat apps follow a simple pattern:
- Send the prompt
- Wait for the full response
- Render everything at once…
AI models have become incredibly fast. Network latency has improved. Yet many AI chat apps still feel slow. This isn’t a hardware problem or a model problem.
It’s a user experience problem.
The Real Problem: AI Chat Apps Feel Slow
When a user sends a message and the UI stays blank even briefly the brain interprets that silence as delay.
From the user’s perspective:
- Did my message go through?
- Is the app frozen?
- Is the model slow?
In most cases, none of this is true.
But perception matters more than reality.
Latency in AI apps is psychological before it is technical.
Why Waiting for the Full Response Breaks UX
Many AI chat apps follow a simple pattern:
- Send the prompt
- Wait for the full response
- Render everything at once
Technically, this works.
From a UX standpoint, it fails.
Humans are extremely sensitive to silence in interactive systems. Even a few hundred milliseconds without visible feedback creates uncertainty. Loading spinners help, but they still feel disconnected from the response itself.
This is the difference between:
- Actual latency → how long the system takes
- Perceived latency → how long it feels like it takes
Most AI apps optimize the former and ignore the latter.
Streaming Is the Obvious Fix and Why It’s Not Enough
Streaming responses token by token improves responsiveness immediately.
As soon as text starts appearing, users know:
- The system is working
- Their input was received
- Progress is happening
Technologies like Server-Sent Events (SSE) make this straightforward.
However, naive streaming introduces a new problem.
Modern models can generate text extremely fast. Rendering tokens as they arrive causes:
- Bursty text updates
- Jittery sentence formation
- Broken reading flow
For example, entire words or clauses can appear at once, breaking natural reading rhythm.
At that point, the interface is fast but exhausting.
Streaming fixes speed, but can hurt readability if done carelessly.
The Core Insight: Decoupling Network Speed from Visual Speed
Network speed and human reading speed are fundamentally different.
- Servers operate in milliseconds
- Humans read in chunks, pauses, and patterns
If the UI mirrors the network exactly, users are forced to adapt to machine behaviour.
A better approach is the opposite:
Make the UI adapt to humans, not servers.
Instead of rendering text immediately:
- Incoming tokens are buffered
- The UI consumes them at a controlled pace
- The experience feels calm, intentional, and readable
To do this, I introduced a StreamingTextController a small but critical layer that sits between the network and the UI.
Streaming isn’t just about showing text earlier.
It’s about showing it at the right pace.
How the StreamingTextController Works (Conceptual)
The StreamingTextController exists to separate arrival speed from rendering speed.
Keeping this logic outside the ViewModel prevents timing concerns from leaking into state management.
At a high level:
- Tokens arrive via SSE
- Tokens are buffered
- Controlled consumption at a steady, human-friendly rate
- Progressive UI rendering via state updates
From the UI’s perspective:
- Text grows smoothly
- Sentences form naturally
- Network volatility is invisible
This mirrors how humans process information:
- We read in bursts, not characters
- Predictable pacing improves comprehension
- Reduced jitter lowers cognitive load
What this controller is not
- Not a typing animation
- Not an artificial delay
- Not a workaround for slow models
It’s a UX boundary translating machine output into human interaction.
Architecture Decisions: Making Streaming Production-Ready
Streaming only works long-term if it remains stable and testable.
Responsibilities are clearly separated:
- Network layer → emits raw tokens
- StreamingTextController → pacing & buffering
- ViewModel (MVVM) → lifecycle & immutable state
- UI (Jetpack Compose) → declarative rendering
Technologies used intentionally:
- Kotlin Coroutines + Flow
- Jetpack Compose
- Hilt
- Clean Architecture
The goal wasn’t novelty.
It was predictable behaviour under load and across devices.
Common Mistakes When Building Streaming UIs
Some easy mistakes to make:
- Updating the UI on every token
- Binding rendering speed to model speed
- No buffering or back-pressure
- Timing logic inside UI code
- Treating streaming as an animation
Streaming is not about visual flair.
It’s about reducing cognitive load.
Beyond Chat Apps
The same principles apply to:
- Live transcription
- AI summaries
- Code assistants
- Search explainers
- Multimodal copilots
As AI systems get faster, UX not model speed becomes the differentiator.
Demo & Source Code
This project is open source and meant as a reference implementation.
It includes:
- SSE streaming setup
StreamingTextController- Jetpack Compose chat UI
- Clean, production-ready structure
Final Takeaway
- Users don’t care how fast your model is.
- They care how fast your product feels.
- Streaming reduces uncertainty.
- Pacing restores clarity.
- Good AI UX sits at the intersection of both.