Build your first ChatGPT app prototype

Start with a small task and a template. This walkthrough takes you through the visual editor, local preview and deployment preparation. Real data connections and ChatGPT host testing need their own checks.

What you will build

A focused interface for one task, such as browsing a catalog or checking a service status. Begin with fixture data, then review a supported connection and deployment requirements before testing the hosted app in ChatGPT.

Before you start

• A DraftYourApp account — the free tier is enough to follow along.
• An idea small enough to finish today. Resist the urge to build everything at once.
• Optional: an API or data source you want to pull live content from.

Step 1 — Start from a template

Open the builder and pick a template that is close to your idea — portfolio, catalog, or services. Templates are not throwaway scaffolding; they are real, polished starting points you reshape into your own app. Starting from one shaves the first hour off your project.

Step 2 — Arrange your UI on the canvas

Drag inputs, buttons, cards, images, and layout blocks onto the canvas and arrange them until the screen reads the way you want. There is no markup to write and nothing to wire up by hand — the canvas is the source of truth for how your app looks inside ChatGPT.

Step 3 — Preview the conversation locally

Open the local conversation preview, inspect your components and simulate actions. Tweak the interface and repeat. The preview does not execute real tools or establish behavior in the ChatGPT host.

Step 4 — Connect real data

For live data, inspect a supported public GET operation from OpenAPI or review selected tools from an existing MCP server. Test the selected operation explicitly and check the runtime requirements before relying on it in a deployment.

Step 5 — Deploy with one click

When the draft passes runtime checks and required provider settings are configured, deploy it to Vercel. Review the hosted release and test it in the intended environment. Technical deployment does not imply OpenAI submission, approval or publication.

Where to go next

You now have a prototype workflow: template, canvas, local preview, reviewed connection and technical deployment. Keep evidence for the version you test and prepare submission material separately if you want public distribution.