Your AI disclaimer is asking the reader to do your QA: AI generated content disclaimer wording

Sunny Ray · Jul 3, 2026 · 5 min read

"If anything is wrong, let us know" is not transparency. It's outsourcing your proofreading to your prospect.

I know because that was roughly my line. Every page I send after a call has a small note at the bottom saying it was built with AI. On July 3 a partner who runs a dev shop told me what that note felt like from his side, and he was right.

A good AI generated content disclaimer says what the thing is, says who stands behind it, and never asks the reader to do work. That's the whole rule. The rest of this post is how I got there.

The feedback: my disclaimer made other people do the work

He had forwarded one of my pages to people he works with. Then he had to explain the errors. His words:

"The onus is now put on whoever you're working with to actually have to fine tune it."

His suggestion was simple. Frame it as what it is, a rough AI summary, not a finished record, so expectations are set before anyone reads a line.

He also gave me a number worth remembering, paraphrasing from the notes: AI summaries get to something like 60 to 80 percent accuracy fast, but getting from there to a reliable record takes real human review time. The last 20 percent is where all the cost is. My disclaimer had been quietly handing that last 20 percent to strangers.

Why I send a page per call anyway

I'm not going back to long emails. Here's how I put it on that same call:

"putting too much in the email it's just like overwhelming and kind of gross"

So after every call, to customers and partners alike, I send a short email with a link to a page: what we discussed, what's next, the relevant links. It's faster for me, it's easier to read on a phone, and people forward it. That last part is exactly why the wording matters. The page travels to people who weren't on the call and have no context for its mistakes.

The speed is the point. The accuracy is the price. The disclaimer is where you decide who pays it.

Wording that sets expectations without passing the buck

Here's the difference, side by side.

The bad version: "This document was generated by AI. Please let us know if anything is inaccurate." It sounds humble. It actually makes the reader the QA department.

The better version: "A fast summary of our call, drafted with AI and reviewed by me. It's a working note, not the official record. Anything that commits us to something will come in a separate email." It tells the reader what kind of document this is, puts a human name on it, and draws a line around what it can be trusted for.

Three principles behind it:

• Say what it is: a summary, a draft, a working note. Not a record, not a contract.
• Take the responsibility: "reviewed by me" means errors are mine, not yours to find.
• Draw the boundary: numbers, prices and commitments get confirmed separately.

GitLab's public design guidance points the same way: short labels like "Generated by AI" or "Summarized by AI," placed near the content, with a prompt to verify. The label belongs next to the thing it describes, not buried in a footer.

Where AI summaries break and where a human has to check

If the honest number is 60 to 80 percent, the question is which 20 percent. In my experience it's the parts that matter most to the other person: their name spelled right, their numbers, who owns which next step, and anything they said off the cuff that the model turned into a promise.

So that's where I read before I send. Names, numbers, owners, commitments. I don't need to polish the prose. I need to make sure nothing on the page says something the other person didn't say.

Repeated AI content reads as spam

The same call caught a second problem. We were building a site for the show with a page per episode, and each page had a sponsored-content section. If we kept it and copied it across our other sites, the same block of similar text would appear everywhere. The call's verdict: that reads as spam and hurts how search engines rank you. We cut the section.

Same story with distribution. Pushing hundreds of episodes to a podcast platform at once would register as spam, so the plan became to trickle them out and lead with fresh content.

The pattern is the same as the disclaimer. AI makes volume cheap. Cheap volume, repeated, looks like a machine talking to nobody. A human has to decide what's worth saying once.

Start small, then scale the AI

The same call gave us a practical way to stop errors before they multiply. For the episode site we wanted clean transcripts on every page. Doing the whole back catalogue in one pass was estimated at around $600 in processing, and nobody would have read the output before it went live.

So the plan changed. Do the first 10 to 20 episodes. Have Charles fine-tune them by hand. Lock the format. Only then run the rest, in batches, and look at a cheaper transcription model to bring the cost down.

That's the disclaimer problem solved upstream. When a human checks the first batch, the template gets fixed once, and every page after it inherits the fix. When nobody checks, the same error ships a few hundred times and the reader finds it for you.

The rule I use now

Disclose that AI helped. Put my name on the review. Never ask the reader to fix my work. And if a page is going to be forwarded to people who weren't there, read it as if I were one of them.

If you want to see what lands in a prospect's inbox after every call we run, see what happens after every call.

Frequently asked questions