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AI Prompt Playground

Test Chrome's built-in, on-device Prompt API (Gemini Nano). Tune the parameters, stream the response, and try schema-constrained JSON output — all privately in your browser.
Your browser doesn't support the built-in Prompt API yet.
This playground uses Chrome's on-device Prompt API (LanguageModel). Use the latest desktop Chrome and enable chrome://flags/#prompt-api-for-gemini-nano, then restart. The model downloads once and runs fully offline — nothing you type leaves your device.
Sets the model's role for the whole session (sent as initialPrompts).
Advanced parameters
Higher = more random and creative. Lower = more focused and deterministic.
Number of candidate tokens considered at each step.
Working…
The model's response will appear here.
Runs on-device — nothing you type leaves your browser
About this tool: It's a thin wrapper over the Web Platform LanguageModel API. Temperature and Top-K map to the model's sampling parameters, the system prompt becomes the session's initialPrompts, and the JSON schema is passed as responseConstraint to force structured output.

Frequently asked questions

Is the AI Prompt Playground free?

Yes, it is completely free. There is no API key, no account, and no usage limit, because the model runs on your own device rather than on a paid cloud service.

Does my prompt stay private?

Yes. The playground uses Chrome's built-in, on-device Prompt API (Gemini Nano), so everything you type is processed locally in your browser. Nothing you write is sent to a server, and it keeps working offline once the model has downloaded.

What do I need to run it?

Use the latest desktop Chrome and enable the on-device Prompt API flag, then restart the browser. The model downloads once, after which generation happens fully offline on your machine.

What do temperature and Top-K control?

They shape how the model samples words. A higher temperature makes answers more random and creative, while a lower one keeps them focused and predictable. Top-K limits how many candidate tokens are considered at each step.

How does the JSON schema constraint work?

Turn on the JSON toggle and paste a JSON Schema. The model is then forced to return JSON that matches your structure, which is handy for extracting clean, structured fields such as product attributes or sentiment labels.

What can I use it for in e-commerce?

Prototype prompts for product descriptions, customer-message replies, tagging or classification, and test the output privately before wiring the same logic into your store workflow.

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