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AI & Machine Learning

LLM Chat Bubbles: Why Are They Out of Order?

Learn how to fix out-of-order LLM chat bubbles caused by async UI issues. Discover stable solutions for updating chatbot messages correctly.

byDev Solutions

December 23, 2025

Chaotic LLM chat interface showing message bubbles out of order with a frustrated developer and async code artifacts.

⚠️ LLMs return answers at different speeds. This makes chat bubbles show up out of order.
🧠 Data like messageIndex and timestamp can put messages back in order, no matter when they arrive.
💡 UI frameworks like React might redraw things based on when answers arrive, not the right order.
🔄 Streaming needs tokens to match the first prompt message exactly.
🪛 Putting backend order with frontend sorting makes the chat work best.

Building chat apps with large language models (LLMs) offers new opportunities. But it also creates specific problems, especially with user interface updates that don't happen at the same time. One annoying problem is when LLM chat bubbles show up in the wrong order. This breaks the natural flow of conversation. In this article, we'll explain why generative AI chat interfaces often have this problem. We'll also show how developers can stop or fix it. They can do this with good state management, by using message data, by planning how to stream, and more.

Why Async UI and LLM Chat Bubbles Don’t Mix Naturally

In chat apps that use large language models like OpenAI’s GPT-4, every message a user sends starts a request that doesn't happen at a set time. The time it takes to get an answer back can change a lot between messages. This depends on how busy the model is and how the network works.

Frontend apps usually use frameworks like React, Vue, or Svelte. All of these quickly react to changes in data. But without safety measures, these frameworks will show elements based on when data becomes ready. They don't always show them in the right order of the conversation.

Consider the following scenario:

User sends messages A, B, and C in quick succession.
Message C gets an answer first. This happens because of network or processing differences.
The frontend immediately shows answer C before A or B.

From the user’s perspective, this is disorienting. A chat interface must feel like a smooth, back-and-forth talk between a person and the model. When LLMs power generative AI chat, if this feeling breaks, it makes the app harder to use. It also makes the app seem less smart.

What’s Going Wrong Behind the Scenes

This problem comes from many parts of the system:

1. Request Queue vs. Response Order

Outgoing requests are usually lined up and sent in order. But the server handles them at different times and on their own. This means:

Requests A, B, C hit the server in order.
Responses return as C, A, B.

LLM chat often shows this pattern. This is because creating and streaming tokens happen at different speeds. The speed depends on how complex the prompt is, how many tokens there are, the computer power available, and content rules.

2. Streaming Complicates Timing

Many LLM APIs today, especially OpenAI and Anthropic, let you stream tokens. This makes things feel faster and less delayed. But it also breaks up the data flow:

Message A might take 500ms to stream its first token.
Message C might start streaming after 100ms.

Even if the first requests are in order, streaming makes them look out of order.

3. UI Reactivity Is Naive Without Context

React, Vue, Angular, and similar frameworks usually redraw parts of the screen when their data changes. Without any sorting or knowing the background, they just update whatever they get first:

If bubble “C” gets data first, it shows first.
This causes "jumping bubbles" effect or wrong visual order.

All these problems together make the chat experience messy and unsure.

Use Message Metadata to Preserve Order

To handle this well, give each message some data that helps sort it in a sure way. This lets your frontend keep messages in the right order. It works even when messages don't arrive in order.

Recommended Metadata Fields:

id – a universally unique identifier (UUID) or sequence-safe string
timestamp – taken when the message is made on the user's side
messageIndex – given one by one as messages are sent
parent_id – for talks with replies or nested answers

Adding this data lets the client sort things. It also makes it easier to get the conversation back in the right order.

🔍 Slack Engineering suggests using very precise timestamps to keep messages consistent. This works even for many messages and across different systems.

Add an Incremental `messageIndex` to Each Prompt

A stronger way is to do more than just use timestamps. You can give each message a clear, increasing index number when users send them. Timestamps show when a message was sent. But they might be the same during many fast events. Or they might not be detailed enough, depending on your system.

Here's how that could look in JavaScript:

const newMessage = {
  text: userInput,
  index: lastMessageIndex + 1,
  timestamp: Date.now(),
  id: uuid(), // Ensure global uniqueness
};

By keeping a messageIndex, your UI can tell messages apart. It works even when timestamps are the same or almost the same.

✅ Best Practice: Wait to show AI answers until all earlier-indexed messages are ready. This stops the UI from jumping around.

Manage Chat State with Precision

Once you're adding ordering data to messages, keep them in a clear, reactive storage:

React: Use tools like useReducer, useContext, or Redux.
Vue: Use Vuex or the Composition API with reactivity.
Svelte: Use reactive variables or stores.

Arrange your message list so that:

All messages are kept in one messages list, sorted by messageIndex.
Any streamed updates change the content of the right message bubble using id or index.
Display loops (v-for, map(), etc.) show sorted lists.

Show loading signs for AI answers that are not ready yet:

const loadingMessage = {
  id: uuid(),
  index: newMessage.index + 1,
  isLoading: true,
  content: "..."
};

Replace this bubble once the actual content arrives.

🧼 Good UI logic makes sure what you see is steady, even if updates happen at different times.

Handling Streaming Responses

Streaming makes things feel faster. But it needs very careful control of message content. OpenAI’s token-by-token stream means you add text pieces in real time to message bubbles that are already there.

Here's a simplified real-time example:

socket.on('new-token', (token, messageId) => {
  updateMessage(messageId, prev => ({
    ...prev,
    content: prev.content + token,
    isLoading: false
  }));
});

Key things to keep in mind:

Use message id to send incoming tokens to the right place.
Only make one message bubble for each request. Don't make new ones for small pieces.
Keep the cursor in place if needed. This helps users not get lost.

Real-Time Protocol Tips: WebSockets vs HTTP

Transport method affects how you should structure your responses:

WebSockets

It's better to use structured messages:

{
"type": "reply",
"id": "abc123",
"index": 5,
"content": "Sure, here's what I found..."
}

Add logic to try again or resend if events are missed.
Hold tokens if the message ID hasn't been set up on the user's side yet.

HTTP API Calls

If answers are broken across many requests, group them and put them in a queue on the backend. Then send them out in the right order.
For long answers or streams, use chunked HTTP or SSE (Server Sent Events) to keep tokens flowing steadily.

Sort Bubbles on Render (As a Backup Strategy)

If nothing else works, sort your chat bubbles when you show them. This means the computer has to do more work. But it makes sure the order is right as a last step:

messages.sort((a, b) => {
  return (a.index ?? 0) - (b.index ?? 0) || a.timestamp - b.timestamp;
});

Lessen the effect on speed by:

Memoizing render output with useMemo in React.
Limiting or delaying how often you sort and show things.
Using layered showing: one step for how things are placed, one for movement.

Caution: Slow reorders make the UI flicker more. Stop bubbles from moving visually unless you really have to.

UX Best Practices for Chat Coherence

How messages flow isn't just about tech. It's also about how people feel. Even if everything works, if it feels slow or out of order, it can make the experience worse.

Design for clarity:

✅ Always show something (like a typing sign or loading spinner) while waiting for the LLM to answer.
🚫 Don't make bubbles move away if they are replaced. Make them fade in instead.
🚦 Wait to add AI answers until they hit a clear point (like the first full sentence).

Studies show that keeping message positions still makes chat apps easier to think about. Don't let your UI accidentally confuse users.

Backend Helps Too—Don’t Leave It All to the Frontend

Frontend solutions handle what users see. But the best fix often needs the backend to help too.

Server-side best practices include:

Add index, timestamp, and parent_id to every message made.
Clearly tell message IDs before starting token streams.
Support queues that deliver in order. This is key when using a queue system or doing AI work in separate small services.

Small efforts to order things in the backend make frontend work much simpler. This is true especially for many fast requests or big business apps.

Plan for Edge Cases and Recovery

Systems that don't run at the same time often have problems. You need to plan for these things to make them strong:

Timeouts: Set a max time to wait for each message (e.g., 30s). Go to a backup plan or try again if that time passes.
Retries: Wait longer between tries if messages are missing or tokens fail to come.
Backup UI: Show different bubbles that say “The AI is taking longer than expected” or other good backup options.

Also keep track of things like missed messages, how many times you retry, and how long answers take. This helps make the system more reliable over time.

Framework-Specific Code Tips

React

const [messages, setMessages] = useState([]);

useEffect(() => {
  socket.on('response', (response) => {
    setMessages(prev => sortAndMerge(prev, response)); // Custom merging logic
  });
}, []);

Make sure sortAndMerge() uses messageIndex and id to combine or update the right message.

Vue

<template>
  <li v-for="msg in sortedMessages" :key="msg.id">
    {{ msg.content }}
  </li>
</template>

<script setup>
const sortedMessages = computed(() => {
  return messages.value.slice().sort((a, b) => a.index - b.index);
});
</script>

Vue works best when you change reactive data the right way. Also, don't change the DOM directly.

Always Test Your Ordering Logic

Don't wait for users to find small problems with how it works. Add tests into how you build things:

💬 Make many messages happen at the same time.
🐢 Make the network slow or make answers late on purpose.

🔍 Write down each step of a message's life:

console.log({ message, sendTime, receiveTime, renderTime });

Improve your system until no mix of inputs breaks the order you expect.

Final Checklist to Keep Chat Bubbles in Sequence

✅ Assign unique id + incremental messageIndex
✅ Include timestamp for redundancy
✅ Defer AI responses until previous ones are handled
✅ Append tokens to existing bubbles by id
✅ Sort on render as a failsafe
✅ Use placeholders during AI processing
✅ Implement retries and timeouts
✅ Log diagnostic telemetry under load

If done right, your LLM chat bubbles can be like real texting between people. Do it wrong, and your chat UI becomes a jumbled mess users can't follow. Keeping order in your generative AI chat app is as important as making smart answers. It connects the whole experience.