How to Spot AI Slop on Your SaaS Blog

A human editor can rewrite every sentence of an AI draft and still leave the reader with nothing useful. Better prose doesn't create evidence.

For your SaaS blog, the useful test is simple: what can someone understand, decide, or do after reading? AI can help you explain an answer. It can't make up for having no answer to explain.

What is AI slop?

AI slop is low-effort, AI-generated content that sounds plausible but gives the reader little useful information. It fills space with generic advice, recycled claims, or confident answers nobody checked.

An article can read smoothly and still qualify. Imagine a guide to choosing an invoicing tool that tells you to “consider your needs” and “compare available options.” Those sentences aren't necessarily false. They just leave you doing all the work the guide promised to help with.

Useful content narrows the problem. An invoicing guide could explain how to check whether a tool handles a particular billing setup. It could show the setting, describe a limitation, and tell you how to test it before moving your customers.

The difference is what the reader gets, not whether the sentences sound natural.

“AI slop” is an informal label, not the formal policy name in the search guidance discussed here. Google's February 2023 guidance focuses on content quality rather than how someone produced it. That distinction matters when you're deciding what to publish.

Watch for these signs in a draft:

• Advice that could fit almost any product or reader.
• Claims without a source, a test, or a clear explanation.
• Instructions that nobody has followed to check whether they work.
• Long passages that repeat the question without answering it.

None of those checks requires you to guess who wrote the article. You're judging the page in front of you.

A short AI-assisted answer with checked instructions can help. A polished page that circles the question cannot. The reader needs something they can use, not proof that someone spent time typing.

Human editing can't supply missing evidence

Rewriting a generic draft changes the wording, but it doesn't establish whether the advice works. A human-written article can contain the same empty claims as an AI-generated one.

Google's February 2023 guidance makes that distinction explicit. It describes earlier concerns about mass-produced, human-generated content and explains why banning all human-written content wouldn't have made sense. The stated goal was to reward quality.

Apply the same reasoning to your editing budget. If you hire someone to make a thin draft sound less robotic, ask what they can actually change. An editor can improve clarity. They can't verify your product's behavior without access to the product or evidence from someone who used it.

Suppose your draft says your tool makes importing data easy. Replacing “easy” with “straightforward” doesn't help a reader prepare their file.

A useful edit would ask:

• Which file formats does the importer accept?
• What happens when a required field is missing?
• Can the reader check the result before saving it?
• What did you have to fix during your own test?

Those questions call for work outside the draft. Open the product. Run the import. Record what happened. Then give the writer something solid to explain.

You can still pay for editing. Just don't ask editing to solve a research problem. Separate “does this read well?” from “have we established that this is true?”

If you're short on time, cut the article's scope before cutting the checks. A narrow answer you can support is a better use of your week than a broad guide you can't stand behind.

Does Google penalize all AI-generated content?

Google's stated guidance doesn't treat AI use alone as a reason to reject content. Its February 2023 post says the focus is “the quality of content, rather than how content is produced.”

That is narrower than saying AI content is safe to publish without review. The same guidance describes the qualities its systems aim to reward: expertise, experience, authoritativeness, and trustworthiness. A tool's ability to produce clean sentences doesn't establish any of those qualities by itself.

For your SaaS blog, AI can help turn rough notes into a readable explanation. You still need to check whether the explanation matches the product and whether the claims match the evidence.

Keep three separate questions on your review sheet:

1. Is the information accurate enough to publish?
2. Does the page answer the reader's actual question?
3. Does the page meet the relevant search policies?

An answer to one doesn't settle the others. A page can contain accurate but obvious advice. A helpful page doesn't come with a promise that it will rank. A human byline doesn't prove that someone checked the instructions.

Avoid building your process around whether a paragraph “sounds AI.” That asks you to judge style when you need to judge substance. Smooth wording can hide a weak answer, and awkward wording can hide a useful one.

Use AI where you can inspect its work. Ask it to simplify a tested explanation or organize supplied notes. Treat any new factual claim as something to verify, not as a bonus you received with the draft.

You don't need a promise that Google likes your writing tool. You need a page you can defend when a reader asks, “How do you know?”

Publishing more pages can cross Google's spam rules

Google broadened its policy against producing many pages mainly to manipulate search rankings, regardless of how those pages were written. Its 5 March 2024 update addressed spam and low-quality results, including content that feels made for search engines.

Don't reduce that change to “Google banned AI blogs.” The stated concern reaches beyond the tool used to write a page. Human involvement doesn't automatically fix a publishing plan built around filling search results with low-value pages.

For a solo builder, the tempting plan is easy to picture: collect a long list of related searches, generate a page for each, and change enough wording to make them look distinct. That's a proposed workflow, not evidence of what readers need.

Before approving that plan, ask what changes between the pages. A different product name or industry label isn't much of an answer. A genuinely different setup process, constraint, or decision may justify a separate guide.

Use these questions before expanding a topic:

• Does this page answer a different question?
• Do you have evidence specific to that answer?
• Would the page still deserve to exist without the hoped-for search traffic?

These are editorial checks, not official policy tests. They help you inspect the purpose of the work before you commit time to it.

Writing about a phrase people search for isn't automatically spam. The concern described in the update is producing many pages mainly to manipulate rankings. Keep that distinction intact.

You can want readers from search and still put their task first. Start with the answer you can support. Decide how many pages it needs afterward.

What makes an AI-assisted guide useful?

An AI-assisted guide becomes useful when it gives readers a supported answer they can act on. Original tests, relevant screenshots, and checked instructions can supply that substance; AI can help explain it clearly.

Google's February 2023 guidance supports judging quality rather than the production method. It doesn't promise that adding a screenshot or describing a test will earn a ranking. Those choices help you show the reader what your advice rests on.

Imagine you're writing a guide to exporting customer records from your product. “Click export and download your data” may skip the exact question your reader needs answered: what will the file contain?

You could make the guide useful by doing the export yourself and documenting:

• Which records you selected and which settings you used.
• What the exported file included and left out.
• Any preparation the reader needs before following the steps.
• How to check whether the export worked.

Only claim results you actually observed. If you didn't test a particular setting, say so or leave that claim out.

Then give AI your notes. Ask it to turn them into a clear sequence, flag missing details, or shorten a clumsy explanation. Don't ask it to invent the result you wish you'd recorded.

A screenshot also needs a job. Use it to show a control, explain an outcome, or make a difficult step easier to follow. A decorative image doesn't support a claim just because it came from your product.

The aim isn't to dress up a generic article with signs of effort. It's to remove a specific uncertainty for the reader. Build the explanation around that uncertainty, and keep only the evidence that helps resolve it.

How can you check a draft for AI slop?

Review an AI-assisted draft by checking its claims and testing its instructions, rather than guessing who wrote it. Google's February 2023 guidance gives you the relevant starting point: judge content quality instead of its production method.

Begin by writing down the reader's task in one sentence. For example: “The reader needs to know whether they can move their existing records into this product.” That's a proposed brief, not a claim about your product.

Now read the draft against that task. Mark the sentences that help the reader complete it. If most of the page survives only because it sounds professional, you have something to cut.

Use a short review sequence:

1. Check the answer. Can you point to the passage that resolves the reader's question?
2. Check the evidence. Can you support each important factual claim with a source or your own recorded test?
3. Check the steps. Can someone follow them without guessing what you left out?
4. Check the limits. Does the page say when the advice doesn't apply?

A draft can fail this review even when every sentence is grammatically clean. It might explain why importing data matters while never explaining how to prepare the file. It might recommend an approach without naming the conditions that make it suitable.

Don't solve those gaps by asking for a longer draft. Add the missing answer or make the promise smaller.

Keep the review proportionate to the page. A definition needs a clear explanation and sound support. A technical walkthrough needs checked steps. Neither needs extra paragraphs just to look substantial.

Before publishing, remove one passage that repeats the point. Then spend the saved space answering the most obvious unresolved question.

How do you build a useful SaaS blog without writing all week?

Start each article with a question you can answer from work you've already done. Use AI to shape the explanation after you've gathered the facts.

That approach fits the emphasis on original, helpful content in Google's February 2023 guidance. It isn't a shortcut to guaranteed traffic. It's a way to stop commissioning drafts before you know what they can contribute.

For a shipped product, look for questions inside your actual work. A difficult support reply may contain a clear explanation. A feature you tested may reveal a useful limit. A setup task may expose a step your documentation skips.

Choose one of those questions, then make a small packet for the draft:

• The exact question and who needs the answer.
• Notes from the test or work you completed.
• Relevant screenshots and source links.
• Claims you're unsure about and must check.

Ask AI to work from that packet and flag gaps rather than fill them with guesses. Read the output beside the evidence. If a sentence says more than you know, narrow it or remove it.

You can reuse the supported explanation in a short post or video. Keep its limits attached. Turning “this worked in the setup I tested” into “this always works” creates a stronger claim without stronger evidence.

Don't set a publishing schedule that forces you to manufacture an answer. If you only have enough material for a concise guide, publish that guide. You don't owe your calendar a sprawling article.

Open your next draft before you generate another one. Find its main promise, check the answer, and add the missing proof. If you can't support the promise, change it before you publish.