How to Check AI Content Before You Publish

A screenshot of your product can support a feature claim. A paragraph that sounds exactly like you can't.

If you've shipped a SaaS product, you already have something useful to write about: work you can show. The job is to explain that work without letting an AI draft invent extra features, better results, or experience you never had.

Before you polish an AI draft, check whether you can support its claims. Another rewrite won't fix a sentence that was never true.

What does authenticity mean when you use AI?

Authentic AI-assisted content tells the truth about your work, your evidence, and who did what. You can use a model to explain a real product change without pretending it ran the test or spoke to the customer.

The AIAAIC glossary defines authenticity as being genuine, original, and what something claims to be. It connects integrity with honesty in information and processes. That gives you a better standard than asking whether a paragraph sounds human.

For your SaaS content, apply that standard to three questions:

• Did the product do what the article says it did?
• Does the evidence support the full claim, including its limits?
• Does the article describe the writer's experience and AI's involvement honestly?

A model can help turn your release notes into a readable explanation. It shouldn't turn those notes into a customer success story unless you have that customer's actual story.

The distinction matters with first-person writing. “I tested this” describes an action. Don't let the model add it because the sentence sounds more credible. Use it only when you ran the test and can explain what happened.

The same applies to authorship. Checking facts doesn't give you permission to mislead readers about who created the work. If you promise a personally written account, handing the whole job to a model breaks that promise, even if every feature description is accurate.

Write a simple rule for yourself: AI can help explain what you know, but it can't supply missing experience. Then make your publishing process enforce that rule, rather than relying on the tone of the finished draft.

A human-sounding rewrite can't prove your claims

Originality.ai warns that its detector score should be treated as “a signal for review, not proof of authorship or misconduct.” That warning comes from a company selling AI detection.

Keep that limit attached to the score. A detector result doesn't establish whether your product supports a feature, whether your price is current, or whether a benchmark happened. Those questions need evidence about your product.

You don't need to settle whether a sentence sounds machine-written before checking whether it's true.

Imagine a draft says your app works with every major accounting tool. That's a hypothetical example, but the review task is concrete. Open your supported integrations list. Does it justify “every”? What does “major” mean? Can a reader check which tools you mean?

Changing the sentence to sound more casual leaves those questions unanswered. So does adding a founder anecdote around it.

Give style review a smaller job. Let it catch stiff phrasing, repeated ideas, and sentences you wouldn't say aloud. Don't ask it to certify the underlying facts.

Keep these checks separate:

• A style check asks whether the writing is clear and sounds like you.
• A claim check asks whether the evidence supports what the writing says.
• An authorship check asks whether you describe the human and AI contributions honestly.

A draft can pass one and fail another. That's useful information, not a reason to keep rewriting until every warning disappears.

For a solo builder, the practical move is simple: review unsupported claims before spending time on voice. Otherwise, you may polish a paragraph that you'll need to delete once you open the product.

What should you put in an evidence folder?

Build your AI writing folder from records of work you've actually done. Start with release notes, dated screenshots, test results, and the outside sources you expect the article to cite.

The AIAAIC glossary identifies uncertainty about content's origin, authorship, and changes as a threat to authenticity. Keeping the original records gives you a way to check where a draft's claims came from.

You don't need a new app for this. Use a folder beside the draft, with filenames you can understand when you return to it later.

Give each type of record a clear job:

• Release notes: what changed and which version received the change.
• Dated screenshots: what the interface showed in the captured state.
• Test records: what you tried, under which conditions, and what happened.
• Outside sources: the exact page and passage behind a claim about someone else's work.

Add a short note beside anything easy to overread. If a screenshot comes from a development build, say so. If a feature requires a particular plan, keep that limit beside the feature description.

A screenshot of a button supports the claim that the button appeared in that captured interface. It doesn't, by itself, prove that every customer can use it or that the action succeeds.

Choose the evidence after deciding what the article needs to answer. Don't upload a pile of unrelated files and ask the model to find a story worth telling.

For a post about a shipped feature, gather the release note and a current view of the feature. Add a test record if you plan to describe how it behaves. If those records don't support an ambitious claim, narrow the claim before drafting.

How do you make AI check each factual claim?

Ask your AI writer to return each factual claim with the exact file or passage that supports it. Treat the result as a review sheet, not a verdict that the draft is safe to publish.

The AIAAIC glossary includes falsification and misrepresentation among the ways AI can undermine authenticity. A useful check therefore needs to test the meaning of a claim, not just confirm that a link exists.

Use an instruction like this:

Use only the supplied evidence for factual claims. After drafting, list each factual claim, its supporting file or URL, and the exact passage or visible detail that supports it. Explain any limits. Mark missing or unclear support for human review. Don't invent a source or fill a gap from memory.

Have the review sheet include these fields:

• The claim as it appears in the draft.
• The supporting file or exact URL.
• The passage, result, or visible detail that supports it.
• Any relevant date, product version, or test condition.
• What you still need to check before publishing.

A citation must support the sentence beside it, not merely discuss the same subject.

For example, a release note announcing an export feature doesn't support a claim that the export is faster than a competitor's. The comparison needs separate evidence. Without it, delete the comparison or flag it for testing.

Keep opinions distinct from claims about observed results. “I'd choose the simpler setup” is a recommendation. “Customers finish setup faster” describes an outcome you need to support.

Ask for this review before the final polish. It gives you a smaller, clearer job: inspect the claims and their evidence instead of rereading fluent paragraphs and hoping something suspicious stands out.

Unsupported product promises should stop publication

Hold a draft when its pricing, performance, or feature claims lack support. Those sentences tell a reader what they'll pay, what they'll receive, or what they can expect your product to do.

The AIAAIC glossary treats misrepresentation as a failure of authenticity. For your product content, turn that principle into a publishing rule you can actually follow.

You don't need an elaborate approval process. Add a status to each important claim: supported, needs checking, or remove. Don't let “needs checking” quietly become “probably fine” because the draft is scheduled.

When a claim lacks evidence, choose one action:

• Find a current record that supports the full sentence.
• Rewrite the sentence so it matches the evidence you have.
• Remove the claim if you can't support it.

Send unresolved product questions to yourself, not back into an endless rewriting loop. You own the product decision. The model doesn't get to decide that a feature is available because it would make the paragraph work.

Watch the small words. “All,” “always,” “unlimited,” and “instant” make broad promises. If you use them, check the full scope rather than the most convenient example.

Apply the same care to comparisons. “Easier than the alternatives” needs a defined comparison and a reason. If you only know that you removed a setup step, say you removed that step. The narrower sentence gives the reader something concrete to judge.

Your publishing rule should survive a busy day. Write it plainly: no unsupported product promises go live. A delayed post is easier to handle than an article you already know you haven't checked.

How do you keep benchmarks honest over time?

Publish a benchmark with its product version and test conditions, then preserve those details when you reuse it. A result from one setup supports a claim about that setup, not every future use of your product.

The AIAAIC glossary connects authenticity with information being what it claims to be. A benchmark needs enough context for a reader to understand what the number actually describes.

Before a result enters your writing folder, record:

• When you ran the test and which product version you used.
• The input or task you tested.
• The settings and environment that affected the run.
• How you measured the result.
• What the test didn't establish.

Keep those details beside the result rather than in a separate note the writer may never see.

If you tested one file, describe that file and that run. Don't let the draft convert the result into a promise about every upload. If you compared two tools, keep the settings and the basis of the comparison available for review.

Distinguish observations from promises in the sentence itself. “In our test” sets a boundary, but it still needs the test details. It isn't a phrase you can attach to an unsupported number to make it safe.

When you reuse a benchmark in another article, check whether the result still belongs in that context. Has the product changed? Does the new article describe a different task? Would the headline make readers expect a broader result than you measured?

You don't have to rerun every test whenever you write. You do have to avoid presenting an old, narrow result as a current, general promise. If you can't check it, describe its limits or leave it out.

What should you check yourself before publishing?

Open the evidence and compare it with the final draft yourself. An AI-generated claim review is another output to inspect, not independent proof that the article is correct.

Originality.ai explicitly warns against treating its detector output as proof of authorship. Don't stretch any automated check beyond the question it can answer. Your publishing decision still needs a direct look at the product claims and their support.

Start with sentences that could change a buying decision. Check the price and plan limits. Open the product before approving a feature description. Read the test record before repeating a performance claim.

Then inspect the links. Open the actual page and find the passage the draft relies on. Check whether it supports the sentence's full scope, rather than a smaller claim hidden inside it.

Finish with a short pass for honesty:

• Remove first-person experience you didn't have.
• Label hypothetical examples so they can't pass for customer results.
• Keep dates, versions, and test limits attached to the claims that need them.
• Describe AI's contribution honestly wherever the context or your promises require it.

Only then polish the voice. Cut the stiff phrases and read the piece aloud. Make it sound like you without changing what the evidence allows you to say.

For your next post, keep the workflow small. Choose one question your product can help answer. Gather the records, draft from them, and ask for a claim-by-claim review. Resolve every unsupported product promise before scheduling.

The useful habit isn't another rewrite. It's opening the file behind the sentence. Start there, and publish the version you can stand behind.