Prompt Checklist for Engineers and Plant Technicians

Good prompts for engineers and technicians have four parts: context, data, output format and verification. Run through this checklist before sending any request to an AI assistant and you cut generic answers, invented numbers and rework. Each item says why it matters and how to check it.

1. Context: who you are and what the answer is for

The model cannot tell whether it is talking to an operator or a plant director. State your role, area and goal. Why it matters: the same topic needs different depth. How to check: would a new colleague understand the situation from your prompt alone?

Maintenance example: I am a maintenance technician on a bottling line. I need a weekly inspection routine for a conveyor with a 2 hp motor and gearbox, written for an operator with no technical training.

2. Data: supply the material, do not ask from memory

Paste the procedure excerpt, failure list, readings or table. Why it matters: without data the model fills gaps with whatever sounds plausible. How to check: every number in the answer should trace back to something you sent.

Before pasting, strip anything confidential: customer names, drawings with intellectual property, personal data. Use a business plan when the material is sensitive.

3. Task: one clear verb at a time

Summarize, compare, classify, draft, calculate and critique are different jobs. Why it matters: mixed requests produce shallow answers. How to check: does the prompt have one main action? If it has three, split it into steps.

4. Format: say how you want it delivered

• A table with defined columns, ideal for inspection plans and FMEA.
• A numbered list of steps, for work instructions.
• A word limit, for reports and emails.
• Register: technical or shop-floor, with or without acronyms.

Quality example: Classify these 12 nonconformities by probable cause (material, method, machine, manpower, measurement) and return a table with number, cause and a one-sentence rationale.

5. Constraints: what must not happen

Write the limits: use only the supplied data, do not cite standards you cannot verify, say so when information is missing. Why it matters: it reduces invention. How to check: scan the answer for phrases like generally or typically with no basis in your data.

6. Verification: make the model show its work

1. Ask where each number came from.
2. Ask it to list the assumptions it made.
3. Redo critical calculations in a calculator or spreadsheet.
4. Confirm standards and catalog values at the official source.
5. Request a second critical pass: what errors could this answer contain?

Golden rule: the AI drafts, the professional signs. Nothing that touches safety, tolerance or compliance moves forward without human checking.

7. Iterate in short rounds

If the first answer missed, do not start over; say what was off. For example, the routine is too long, cut it to ten items ordered by criticality. Save prompts that worked in a shared folder with a date and an owner.

Once the team has a library of tested prompts, consider building them into processes. See how AI automation can turn recurring prompts into routines.

Frequently asked questions

What is the most common mistake in technical prompts?

Asking without supplying data. With no reference material the model fills gaps with plausible but possibly wrong answers.

Can I paste internal drawings and procedures into AI?

Only in tools with a business contract and a clear policy. For confidential material, remove sensitive details or use a private deployment.