Want your site to be ignored by AI? It's easy. Just one hour of work — and artificial intelligence will surely skip your pages. In this article, we share anti-examples that kill visibility in AI answers. Check if your site has these mistakes.

All examples are from real practice. Don't do this.

Starting Without Substance: First 800 Characters Are Empty

The model looks for the main explanation in the first paragraphs. If they're empty, it loses interest.

Wrong example. In today's world, many entrepreneurs think about promoting their websites. But how to choose the right strategy? Let's figure it out in this article. Promotion is a complex and multifaceted discipline that requires a professional approach.

Why this kills citations. The model can't determine the page topic. It sees questions without answers and generic reasoning. Focus shifts to secondary blocks. The chance the model reads to the useful part drops to zero.

Wall of Text: One Paragraph of 500 Words

The model extracts paragraphs, not sentences. If a paragraph is too long, it sees a 'mass' rather than structure.

Wrong example. One paragraph with over ten sentences mixing definitions, examples, explanations, digressions, and conclusions. No line breaks. Sentences jump from topic to topic. No clear logic.

Why this kills citations. The model can't isolate a complete fragment. Even if there's a useful thought inside, it remains inaccessible for extraction. The model skips the whole paragraph.

Meaningless Headings: 'More Details' and 'Important to Know'

Headings are navigation for loading. If they're vague, the model doesn't understand the structure.

Wrong example. Second heading: 'Introduction.' Second heading: 'More About Services.' Second heading: 'Important to Know.' Second heading: 'Additional Information.' Second heading: 'Conclusion.'

Why this kills citations. The model can't determine which semantic block follows which heading. 'Introduction' — what's in it? Definition? History? General reasoning? 'Important to Know' — what exactly is important? The page looks like an unstructured text array.

Missing Interim Conclusions

An interim conclusion is a short paragraph that sums up a section. Without it, the model may not capture the main idea.

Wrong example. A section ends with an example or clarification. No transition to the next part. The text just stops. Or the section ends with a phrase like 'As we can see, there are many nuances to consider' (without specifics).

Why this kills citations. The model doesn't understand what conclusion it should draw from the section. It can't take a fragment for an answer because there's no final thought. If a section ends with an example, the model might think the example is the main point.

Links Inside Definitions and Conclusions

The model doesn't click links. It ignores them. But a link inside a key fragment distracts its attention.

Wrong example. GEO optimization is configuring content for AI (with a link to an internal page). This means classic promotion no longer works. Or an interim conclusion: Thus, contact us for generative optimization (with a link to the contact page).

Why this kills citations. The model spends attention on identifying the link as an element to ignore. It may not capture the meaning of the fragment. This is especially critical for interim conclusions — the most cited fragment on the page.

Q&A with Artificial Questions

The Q&A block is one of the most cited structures. But only if the questions are real.

Wrong example. 'Why is our company the best choice?', 'How do we care about clients?', 'What are our advantages over competitors?', 'Why do people recommend us?'

Why this kills citations. The model reads such questions as marketing and ignores the entire block. These aren't user questions — they're self-praise. The model won't use answers to such questions because they don't solve a real user problem.

Empty 'About Us' Page

The 'About Us' page is a key trust signal for the model. If it's empty or purely marketing, trust drops. A professional SEO audit for search engines helps identify such issues.

Wrong example. We are a team of professionals with extensive experience. Individual approach to each client. Modern technologies and equipment. Market leaders according to... We work for you around the clock.

Why this kills citations. No verifiable facts. 'Extensive experience' — how many years exactly? 'Team of professionals' — who exactly? Names, qualifications? 'Market leaders' — based on what? The model doesn't trust words without evidence.

Lack of Local Signals for a Business in Moscow

For a business in Moscow, geographic relevance is a key factor for appearing in 'near me' answers. Effective geo-optimization of the site fixes this mistake.

Wrong example. The site shows a generic address: 'Moscow, center.' On maps: the listing is unverified or partially filled. The text doesn't mention districts, metro stations, or landmarks. Working hours: not specified or outdated. No photos of the facade or sign.

Why this kills citations. The model can't confirm where the business is located. Can't determine if it's open now. Can't associate the business with a query like 'in Khamovniki' or 'near the metro.' Without this data, the model won't show the business in local answers.

Conclusion

In an hour, you can completely ruin generative optimization. All you need is: write a vague introduction without substance, create walls of text without paragraph breaks, use meaningless headings, forget interim conclusions, place links in definitions and conclusions, write artificial questions, leave an empty 'About Us' page, and forget local signals.

Check your site. If you find at least three mistakes from this list — you're already losing traffic. AI doesn't cite such pages. Fix them. For new search trends, read the article new search trends.

Frequently Asked Questions

How quickly can I fix these mistakes?
Start with the first 800 characters: write a direct definition. Break long paragraphs into short ones (two to four sentences). Rename headings to meaningful blocks. Add interim conclusions after each semantic part. Remove links from definitions and conclusions. Replace artificial questions with real ones.

Why are these mistakes so critical for AI?
The model doesn't analyze text like a human. It looks for clear, complete blocks it can use as ready answers. If a block is vague, too long, lacks a conclusion, or contains distracting elements — the model skips it. It won't 'fill in the gaps' or 'correct' anything.

Are all these mistakes equally harmful?
No. The most critical is the lack of interim conclusions. Without them, the model may not capture the main idea even with perfect structure. Second is long paragraphs. Third is a vague start. Other mistakes are also harmful but can be fixed later.

How do I check that mistakes are fixed?
Use manual testing. Formulate a key query, ask Alisa or another assistant. See if fragments of your site appear in the answer. If yes — you're on the right track. Repeat the check one to two weeks after fixes.