Stop Overprompting 📉 Do this instead!

Why are engineers writing paragraphs to get a simple table from ChatGPT? There’s a faster, clearer way—and it starts with talking like a human.

What’s Good About It

Here’s what makes conversational prompting surprisingly effective for building engineering calculation templates:

• Cleaner Input Extraction

  You can simply say: “List the input variables from this section, with symbol, value, units, and source.” GPT handles the structure without extra effort.

  
  

• Fast Turnaround for Templates

  Within 2–3 follow-ups, you can turn raw client data or textbook examples into reusable templates—ideal for pumps, line sizing, vessel calcs, and more.

  
  

• Adaptable for Real Workflows

  Instead of rebuilding prompts from scratch, you iterate naturally:“Now turn this into a blank template.”“Add a column for citation format (Ref #, pg #).”“Add formulas where possible.”

  
  

What’s Bad About It

Conversational prompting only works if you're precise in plain terms. Here’s where engineers go wrong:

• Vague inputs like “add sourcing best practices” get vague output

• If you skip unit format or citation formatting, GPT will too

• Doesn’t work well for automation unless you lock the format after testing

  
  

Why It Works for Engineers

Let’s say you’re sizing a centrifugal pump.

Instead of building a GPT from scratch, try this:

1. “Extract all input variables from this datasheet and organize in a table.”

2. “Add formula column and source citation (Ref #, pg #).”

3. “Now turn this into a blank template I can reuse.”

   
   

Within one conversation, you’ve got a standard calc template—and no need to restart or over-prompt.

Conclusion / Call to Action:Stop writing long, rigid prompts. If you can talk like an engineer giving clear steps to an intern, you can build reusable templates with ChatGPT—fast.

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