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August 30, 2026 Our AI pipeline attributed products to companies that never made them. So we made evidence mandatory.

I run PromptZone, a 22K-member AI community. Last year I built a content

pipeline that drafted articles with AI assistance. It worked, until it didn't.

For a while the pipeline generated article intros from a reference example.

The model copied the example's opening sentence verbatim into articles it had

nothing to do with — attributing unrelated products to a company that never

made them. Published. Indexed. Live for a while before anyone caught it.

The obvious lesson is "add a check." That's the wrong lesson. The right one is

that generated output needs evidence attached before it's presented as true.

A check catches the error you thought of. Evidence catches the ones you didn't.

So I rebuilt the prompt directory around one rule: a prompt doesn't get

published as usable unless it has been run, and you can see the run.

Every published prompt is executed in a locked sandbox against its target

model — no tools, no network, no credentials, synthetic inputs only, an exact

model identity with no provider fallback. The complete output of that run is

stored and shown on the prompt's page. Not a summary of it. The output.

Curated prompts additionally pass review on usefulness, accuracy and safety:

three models from three different vendors, one per dimension, and all three

have to pass. That review is done by AI agents, not human editors. I say that

plainly because implying an editorial team that doesn't exist is exactly the

failure mode I just described.

Two implementation details that turned out to matter more than expected:

1. The evidence is bound to a SHA-256 hash of the prompt text. Edit one

character and the badge is invalidated until the pipeline runs again. A

badge that survives edits means nothing.

2. Review is unanimous, not majority. With no human anywhere in the loop,

"two of three liked it" is too weak to put a badge on.

The honest limit: machine review catches a prompt that doesn't run, produces

nothing, or produces something obviously wrong. It doesn't have taste and it

doesn't know your customers. What it gives you is a floor — the prompt works,

and here's what it did.

3 Comments

  1. 2

    The hash-bound evidence is the interesting part here.

    Curious whether users actually treat the run evidence as a trust signal, or whether they still judge a prompt mainly from the output and description.

    1. 1
      Honestly: I don't have the data yet. The directory only just got rebuilt this way and it goes live properly tomorrow, so anything I told you about user behaviour right now would be me guessing with confidence I haven't earned. My hypothesis is that you're right — most people will judge from the output and the description, and the stored run will go mostly unopened. But I think that's fine, because I've come to see the evidence as doing its real work on the supply side rather than the demand side. Its main job isn't to convince a reader. It's that a prompt which doesn't run, or runs and produces nothing useful, can't get published in the first place. The badge is a byproduct of a gate. That reframes the hash-binding too. It's not there so users can verify a hash — nobody is going to do that. It's there so I can't quietly edit a prompt after it passed and keep the badge. It constrains me more than it informs them. What I'll actually measure after launch: whether people expand the evidence drawer at all, and whether prompts with visible output get copied at a different rate than ones where the drawer stays shut. If the answer is "nobody opens it and copy rates are identical," that's a real finding and I'd rather publish it than bury it. Curious what would move you personally — would seeing the run change whether you trusted a prompt, or would you just try it and judge the result yourself?
      1. 1
        That’s useful context. I’d rather continue this conversation privately than go deeper here. What’s the best email to reach you on?

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Every prompt site published strings nobody had run, mine included. So our 22K-member community's directory has one rule: nothing publishes until it's run in a sandbox, output visible. AI agents review, not humans.