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Claude Prompt to Build a Streaming AI Chat UI (ChatGPT-style)

Build a streaming chat UI: token streaming, stop generating, markdown with code highlighting, history, file upload, and error-state retries.

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prompt.txt

                                

What this prompt does

This prompt builds a ChatGPT-style streaming chat interface for [platform] using [model_provider], covering both frontend and backend. It specifies real-time token streaming over SSE or WebSockets, a typing indicator with stream cancellation (stop generating), message history persisted with [history_storage], and Markdown rendering with syntax-highlighted code blocks via [highlight_lib].

The structure works because streaming UX feels simple until you handle the hard edges. Stop-generation, file upload for [supported_files], conversation management (new, rename, delete, search), a model and parameter selector, and a mobile-responsive layout in [ui_framework] are all defined. The error-state step — handling rate limits, network errors, and model errors with retry UI — is where most of the real work lives, because clean cancellation and failure recovery are what make streaming feel polished.

The rendering and history layers are what make the interface feel finished. Markdown with syntax-highlighted code blocks via [highlight_lib] and per-block copy buttons turn raw model output into something developers can actually use, while [history_storage] keeps past conversations searchable and renamable. Keyboard shortcuts — Enter to send, Shift+Enter for a newline, a shortcut for a new chat — are small touches, but they are exactly what makes a chat UI feel fast to power users rather than clunky.

When to use it

  • You are building a chat product on [platform] and want token-by-token streaming, not a spinner then a wall of text.
  • You need stop-generation so users can cancel a long response mid-stream.
  • You want Markdown rendering with syntax-highlighted, copyable code blocks.
  • You need conversation management: new chat, rename, delete, and history search.
  • You want file upload for [supported_files] as conversation context.
  • You need robust error states with retry UI for rate limits and network failures.

Example output

Expect a full streaming chat app: a [backend_framework] backend streaming tokens over SSE or WebSockets, a [ui_framework] frontend with a typing indicator and stop button, Markdown rendering via [highlight_lib] with copy-code buttons, [history_storage] persistence, a model and parameter selector, file upload, keyboard shortcuts, and error states with retry. It usually covers the responsive layout and cancellation handling end to end.

Pro tips

  • Choose SSE over WebSockets unless you need bidirectional streaming; SSE is simpler for one-way token streams.
  • Get stop-generation right early — abandoning a half-streamed response cleanly is harder than it looks and users expect it.
  • Use a fast syntax highlighter for [highlight_lib] so large code blocks do not jank the stream as tokens arrive.
  • Keep [history_storage] client-side first if privacy matters; sync to an API only when cross-device history is needed.
  • Handle every error state — rate limit, network drop, model error — with a clear retry, since failures during streaming are common.
  • Wire keyboard shortcuts (Enter to send, Shift+Enter for newline) early; they are core to a chat UI feeling fast.
  • Add a per-block copy-code button; for a developer-facing chat it is one of the most-used affordances in the whole UI.
  • Expose the model and parameter selector (temperature, max tokens) so power users can tune behaviour without code changes.

Frequently Asked Questions

Should I use SSE or WebSockets for streaming?
The prompt supports either. SSE is simpler and well-suited to one-way token streaming from server to client, which covers most chat UIs. WebSockets make sense when you also need real-time messages flowing back up, but that bidirectional capability adds complexity you may not need.
How does stop generating work?
The design includes stream cancellation so a user can halt a response mid-generation. Implementing it cleanly means aborting the underlying request and closing the stream without leaving the UI in a half-rendered state, which is one of the trickier parts of building a polished streaming interface.
Does it handle network failures during a stream?
Yes. Error states for rate limits, network errors, and model errors are explicitly included, each with retry UI. Failures mid-stream are common, so handling them gracefully rather than freezing the interface is a core part of what separates a polished chat UI from a fragile demo.
Where is conversation history stored?
It uses `[history_storage]`, which by default is client-side IndexedDB with optional API sync. Keeping history on the client first is good for privacy and offline access, while the API sync layer lets users carry conversations across devices when that matters for your product.
Engr Mejba Ahmed

Need this built for real?

Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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Engr Mejba Ahmed

Engr Mejba Ahmed

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