How to check AI-written content before publishing

A release note can support a feature announcement. It can't prove that your customers saved time.

That gap is where your AI publishing process needs a check. Let the model turn product knowledge into drafts. Before you polish the writing, make it show which claims your evidence actually supports.

What is ai generated content?

AI-generated content is text, images, audio, video, or code created by models that learn patterns from training data. Google's explanation of generative AI describes models learning through observation and pattern matching, then using those patterns to create new content.

For your SaaS, that could mean a draft article, an illustration, or a script for a product video. It could also mean a suggested code example inside your documentation. The format changes, but you still need to check what the output claims.

A model's ability to write a convincing sentence doesn't establish that the sentence is true about your product. Treat its output as something to review, rather than as a record of what happened.

Keep three things separate when you publish:

• The source: the document, measurement, or approved quote that supports a claim.
• The draft: the model's attempt to explain that information.
• The published claim: the wording you checked and chose to stand behind.

A model can help with the middle step. You still own the last one.

AI use also comes in degrees. Asking a model to fix a sentence differs from asking it to create an article from a short prompt. Australia's National AI Centre recommends considering how much AI contributed when deciding how to disclose its use.

For a small software business, a useful starting rule is simple: use AI to explain what you know. Don't ask it to supply the customer results you wish you had.

What should you let AI draft for your SaaS?

Let AI draft explanations from material you've already checked. Google's guide to generative AI describes tools that create content from learned patterns; that ability doesn't give a model firsthand knowledge of your customers or your current product.

Start with a writing task whose facts you can inspect. A release note can become a feature walkthrough. A support answer can become a troubleshooting article, provided you remove private information and check that the instructions still work.

Useful drafting jobs include:

• Explaining an existing feature in plain language.
• Turning approved documentation into a how-to article.
• Adapting a checked article into social posts.
• Suggesting clearer headings for a draft with established facts.

Keep a tighter boundary around customer stories. If you don't have an approved quote or a documented result, don't ask the model to fill the gap with something realistic. A plausible customer story is still an invented customer story.

The same boundary applies to product promises. Documentation showing that a feature exists doesn't establish that it will improve every buyer's results. Ask the draft to describe what the feature does before making claims about what someone will achieve.

Don't mistake the supplied Google help page for a search-ranking policy, either. It explains generative AI. It doesn't establish that a particular publishing process will earn search traffic.

Your goal should be a useful page you can defend, not a promise that AI-written pages will rank. Choose a question you can answer from real product knowledge, then let the model help you answer it clearly.

Build a source pack before asking for a draft

Give the model the evidence you want the article to use before it starts writing. Google's description of model training explains how models learn patterns; it doesn't establish that they know your latest release, approved customer wording, or internal measurements.

A small source pack gives you a clear boundary for the assignment. Keep it narrow enough that you can check every item yourself.

For a product article, I'd include:

• Current product documentation, with the relevant passages marked.
• Release notes that show what shipped and when.
• Customer quotes you have permission to publish, copied exactly.
• Dated measurements with an explanation of what you measured.

You don't need every document your business has ever produced. You need enough evidence to answer the reader's question without guessing.

Add limits beside the evidence. If a feature only works on a particular plan, include that restriction. If a result came from one customer's setup, keep that context attached. If documentation describes a planned feature, don't let the draft call it available.

Also mark what the source pack doesn't establish. A measurement might show elapsed time without showing whether the work was better. A quote might describe one person's experience without supporting a broader claim.

A useful instruction is: “Use only these sources for product claims. If they don't support a statement, flag it instead of completing it.”

That instruction isn't a guarantee. It gives you a specific check to perform. You can compare the output against a known set of documents instead of investigating every polished sentence from scratch.

Flag unsupported claims before editing the prose

Check customer results, product capabilities, and numbers before spending time on the draft's style. The National AI Centre warns that laws on misleading information, privacy, and online safety may apply to AI-generated content. A well-written sentence doesn't remove those concerns.

Ask the model to attach evidence to each claim that could affect someone's decision to buy or use your product. Keep those notes in the working draft, even if the final page won't show every internal reference.

For each claim, ask for:

1. The exact sentence it wants to publish.
2. The source and passage it relies on.
3. Any limit the reader needs to understand.
4. A clear flag if the source doesn't support the wording.

Then inspect the match yourself. A source link isn't enough. The linked material has to support the actual sentence.

For example, imagine your documentation says a feature lets users schedule reports. That supports a statement about scheduling reports. It doesn't, by itself, support a statement that customers finish their reporting faster.

Similarly, an approved customer quote supports what that customer said. It doesn't automatically prove that every buyer should expect the same result. Keep the speaker and the context visible.

Review the claim before you review how good it sounds.

You can make a clumsy sentence clearer after you know it's true. If you start by making it persuasive, you risk spending your limited review time defending wording that should never have made the draft.

Delete an unsupported claim, narrow it to what the source shows, or get the missing evidence. Don't ask for a more confident version.

Rewrites need the same evidence as the first draft

A shorter headline still needs to stay within what its source supports. The National AI Centre includes AI-created and AI-changed content in its guidance, so your review shouldn't stop once the first draft passes.

When you ask for a punchier rewrite, keep the evidence beside the sentence. Make preserving the claim's limits part of the assignment.

Consider an illustrative edit from “can help you prepare a report” to “guarantees faster reporting.” The second sentence adds both certainty and a result. You need evidence for those additions, even if the wording feels like a minor copy change.

Check for changes in:

• Who: did one customer's experience become a claim about all customers?
• Certainty: did “can” become “will” or “guarantees”?
• Scope: did a result from one task become a promise about the whole job?
• Conditions: did the rewrite remove a plan limit, setup requirement, or date?

Keep the source notes attached when you turn the article into other formats. A social post has less room for context, so choose a narrower claim rather than squeezing out a necessary limit.

Give headlines their own review. Readers can encounter a headline without reading the explanation underneath. Don't rely on a careful paragraph to rescue a promise the headline can't support.

The same goes for a video hook. You can make it direct without making it absolute. After the final edit, compare the published wording with the original evidence one more time. Check what survived, not just what you approved earlier.

When should you disclose AI-generated content?

Choose how to disclose AI use by considering both its contribution and the content's potential impact. Australia's National AI Centre says you may not need to disclose every use; the right approach depends on the context.

That is more useful than treating a small spelling edit and a mostly generated article as identical. It also asks more of you than putting the same vague AI label on everything.

Before publishing, ask:

• How much did AI create or change?
• Could the content influence a consequential decision?
• Could a reader misunderstand who created it or where its claims came from?
• Would a clearer or more visible disclosure help the reader judge it?

The guidance calls for clearer or more visible methods when content could affect decisions, rights, safety, or trust. It names health, hiring, financial and legal information, and public communications as examples.

For your SaaS, examine what the individual piece asks readers to believe. A product illustration and an article offering financial information raise different questions. Don't decide solely from the file format or the fact that both came from the same tool.

Where disclosure is appropriate, describe what actually happened. If AI helped draft the text and you checked the product claims, say that only if you did both. Don't imply that a person verified something when nobody checked it.

Disclosure explains how you made the content. Evidence supports what the content says.

Neither replaces the other. An AI label doesn't make an invented customer result acceptable, and a checked fact doesn't settle every question about how clearly you should disclose AI use.

How do you run this process without spending all week on it?

Use one checked source pack to create a draft, review its claims, and then adapt it into other formats. Google's generative AI guide describes tools that can create content; your publishing process should give that drafting ability clear limits.

For a solo founder or tiny SaaS business, I'd keep the process short:

1. Pick a customer question you can answer from existing evidence.
2. Gather the documents and approved examples needed to answer it.
3. Generate a draft with sources attached to product claims and results.
4. Resolve unsupported claims before editing for voice.
5. Recheck the final wording and choose any appropriate disclosure.

Don't add a separate tool just to make this look organised. A shared document can hold the draft, its source links, and the claims that still need checking. Use whatever lets you inspect the evidence without hunting through chats.

Keep unchecked claims out of the publishing queue. If a sentence depends on a measurement you haven't made, remove it or pause the piece. A deadline doesn't turn a guess into a result.

Once the main article passes, use it to draft your shorter posts. Carry the source notes with it and check that each shorter version preserves the limits. You don't need every format to repeat every point.

If time runs out, cut an extra format before cutting the evidence check. Publishing fewer defensible claims is a better choice than rushing out promises you can't support.

For your next article, change one thing: ask the model to show the source beside every product claim before you read for style. Start your review with the sentences that have nothing beside them.