In a demonstration I recorded for class, I gave ChatGPT a short assignment: create a seven-slide networking presentation for a fictional company called Country Garden Supply. Include a title slide, a network diagram, and static IPv4 addresses.
That sounds like an underspecified prompt. Where were the business requirements? What was the assignment? How would the model know what belonged in the presentation?
The answers were in the project folder. Before assigning the presentation, I asked ChatGPT what the final project required. It found and read an assignment page I had saved as an HTML file in my Intro to Networking folder. That document supplied details I did not have to dictate again.
Local projects in the ChatGPT desktop app can give the system access to folders on your computer. Those files become available for searching, reading, and editing. Access does not mean every document has already been read or that the correct version will always be selected. OpenAI’s project documentation explains how this works.
Watching the demonstration later, I noticed myself saying that I had barely provided any context. But I had. I had prepared the folder.
That is a useful shift in how we think about prompting. The request in the chat box is only part of the assignment. The requirements document, spreadsheet, template, and reference materials can carry much of the detail.
For business work, prepare those materials deliberately. Identify the current policy. Label the approved template. Separate old drafts from the version that should govern the task. Tell the agent which source takes precedence when documents disagree.
Then ask it to summarize the requirements before it starts building. That gives you an early opportunity to catch the wrong file, an outdated instruction, or a missing constraint. The next improvement to your AI workflow is fifteen minutes spent organizing the co-work folder.
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