ChatGPT can help turn a written requirement into application code. What happens to that code depends on the tools and environment available in the conversation.
A generated screen, a saved project, and a publicly reachable application are different deliverables. Agree on which one you want before asking the agent to build. Otherwise, “done” can mean a plausible example in the chat while you expect a working service.
Give the conversation a destination
For a business app, specify where the code should live, where the application should run, which data it can use, and who may use it. Keep passwords and private customer records out of the prompt. Use the platform's authentication flow and synthetic examples for development.
OpenAI documents MCP-backed tool integration as a way to expose external capabilities. The available tools determine what the assistant can inspect or change. A connection does not by itself prove that a particular account can deploy to your chosen destination.
Before making changes, ask the assistant to identify the connected system and target application. Read the answer. A familiar name in a conversation is weaker evidence than an identity response from the actual service.
Request a result you can inspect
Here is a useful build request for a service-request application:
Create a request form with category, description, and contact fields. Save valid requests in the application database. Show a confirmation containing the saved request identifier. Reject a missing description. Do not email anyone yet. Show the stored record and the validation result when finished.
The request contains both a visible outcome and evidence behind it. It also leaves an external action out of the first release deliberately. You can review the form and data before authorizing notifications.
Once the basic flow works, add access rules and test them with separate accounts. A browser showing a success message does not establish that a record was saved or that another user is prevented from reading it.
Separate design help, implementation and deployment
At the design stage, ask for records, screens, and unresolved rules. You should be able to correct the proposal without deploying anything. A useful response might identify a request record, a status history, and a question about who can close a request.
At the implementation stage, the agent needs a connected place to save code and run the relevant checks. Ask it to identify the files or platform artifacts it changed. If its environment can only return code in chat, someone still needs to place that code into a project and operate it.
At deployment, ask which environment will receive the change and what the save or deploy operation does. Some tools create a private preview; others change a live application. Establish that behavior before authorizing the call. A private draft requirement belongs in the working instructions and must match the destination's actual capabilities.
For the service-request example, the handover should let you connect the form you used to the record it saved. It should also identify any unfinished work, such as notifications or staff access. If the answer only describes how the app would work, request the missing implementation or narrow the claim to the design that was actually produced.
What still belongs to you
You choose the business rules, authorize consequential actions, and decide whether the result is suitable for the people using it. Generated code also needs maintenance. Someone must own changes when an upstream API or your internal process changes.
You should receive the implementation, a record of the checks performed, and a clear list of anything unverified. Avoid accepting an answer that describes intended behavior as if it had been observed.
OBTO provides a destination where an agent can inspect and edit application records through MCP. Its connection guide explains the setup. Client availability and organization policy can affect the connection path, so check your actual account before planning around a specific integration.
Start by asking the assistant to inspect the target and propose a bounded change. After it builds, open the application yourself and complete the task you wrote in the brief.