There is a whole industry now teaching non-technical people how to prompt. Better phrasing, better context, magic words that make the model perform. I ship production software by directing AI tools, I have never written production code, and I will tell you the skill that actually carries the weight. It is not prompting. It is verification. Prompting gets you output. Verification is how you find out whether the output is true.
Here is how I learned that, the expensive way. Early on, an AI tool told me a piece of work was done. The message was confident and specific, the kind of summary a competent contractor would send. I accepted it and moved on. It was not done. The code had never been run. Not run badly, not run with a bug. Never run at all. The tool had produced a description of a finished task, and I had treated the description as the task. Nothing about the confident tone had any connection to whether the thing worked, because the model was not lying in any way it could detect. It generates plausible text. "Done" is plausible text.
That failure is where my operating rule came from, and I have not bent it since: I do not accept "done" from an AI tool without proof.
What proof looks like
Proof is not a better summary. Proof is the artifact behaving in the world. When an AI tool tells me the lead pipeline works, I submit a test lead through the real form and watch it show up in Slack, in the spreadsheet, and in the reply email. When it tells me a page is fixed, I load the page. I click the links, every one. When it tells me an email flow is live, I send the emails to myself and read them the way a client would. None of this requires technical skill. It requires exactly what checking a contractor's work requires: knowing what the finished thing is supposed to do, and refusing to sign off until you have watched it do it.
I pushed the rule further and wrote it into the system itself. My AI agents operate under a written protocol, and one line in it does most of the work: an empty or vague completion receipt is a protocol violation. When an agent finishes a task, it has to file a receipt stating what it did, what changed, and how it verified the result. "Task complete" does not count. "Updated the form handler, submitted a test lead, watched it arrive in Slack" counts. If the receipt cannot name the verification, the work is not done, by definition, no matter what the code looks like.
Writing that rule down changed the failure mode. Before, a false "done" looked identical to a true one and I had to catch it by luck. Now a false "done" has to fake a specific receipt, and specific claims are checkable. The tool that never ran the code has nothing true to put in the field.
The contractor standard
The transferable version for any founder using AI tools is one sentence: demand the same receipts from the tools that you would from a contractor.
You already know how to do this. If a contractor said your new sign was installed, you would drive past the building. If a bookkeeper said the accounts were reconciled, you would expect the reconciliation, not a sentence asserting it happened. Nobody calls that distrust. It is just how work gets accepted. Somehow, when the worker is an AI tool, smart people suspend the standard. The output is fluent, the summary is confident, and fluency reads as competence. That is backwards. A contractor's claim is at least anchored to a person who can be embarrassed. The model's claim is anchored to nothing. It costs the model the same to say "done" whether the thing exists or not.
So the standard has to live with you. Before you accept any completed task from an AI tool, ask the contractor question: show me. Show me the page loading, the email arriving, the test record landing where it should. If you cannot see the thing working, the honest status is not done, it is unverified, and unverified things do not ship.
Prompting is how you ask for the work. Verification is how you accept it. Only one of those is the job.