Run a Sizing Sanity Check on a Customer's Spec Sheet with ChatGPT

Confidentiality caution: A customer's spec sheet can carry their name, site details, or internal drawing numbers, and for defense, aerospace, or semiconductor products it may also contain export-controlled technical data under ITAR or EAR rules. That kind of data cannot go into a consumer chatbot at all. Before you upload anything, confirm what classification applies. If you're not sure, check with your export control or legal contact first rather than guessing. This step matters more than any of the ones that follow it.

Once you've confirmed the file is safe to use in a consumer AI tool, open it and remove or replace:

1. The customer's name and site address, replaced with something generic like "Site A."
2. Any drawing or document numbers that trace back to a specific project.
3. Anything else that would let a reader identify the customer if the chat were ever seen by someone else.

1. Open chatgpt.com and start a new chat.
2. Look for the attachment control in the message box, commonly shown as a paperclip icon, and use it to select your cleaned-up spec sheet file.

What you should see: ChatGPT should build an interactive table view of the uploaded data once it processes the file, letting you scroll through the rows and columns it read. Troubleshooting: If the upload option isn't available, your account may need Plus for full file handling, or the file type may not be supported. Try converting a scanned PDF to a text-based PDF or CSV first.

Give ChatGPT the specific requirement to check against, not just the file.

Exact text to copy and adapt:

This spreadsheet has the customer's stated requirements: flow rate, pressure, and duty cycle in the columns you can see. Using [your catalog's sizing formula or rule, described in plain terms], calculate the minimum unit size that meets these requirements for each row. Flag any row where the current selected model in the "Proposed Model" column looks undersized or oversized against your calculation, and show your math.

What you should see: A row-by-row calculation, usually with the underlying arithmetic shown, and a flag on any row where the math doesn't match the proposed model.

Verification step: Pick one flagged row, or if nothing was flagged, pick one row at random. Recompute that single sizing figure yourself using your catalog's published formula or sizing table, by hand or in your own spreadsheet, and compare it to ChatGPT's number. If the two don't match, don't trust the rest of the output until you understand why. A single spot check catches a wrong assumption (a unit conversion, a rounding rule, a misread column) before it spreads across every row.

For any row ChatGPT flagged as a mismatch, confirm against the catalog page or spec sheet for that model whether the flag is real or the AI misread a column. Only carry a correction into the actual quote once you've checked it against the catalog yourself.