How to work with AI texts to stay at the top
Just a few years ago, texts generated by artificial intelligence were considered a novelty. Now they have become a common working tool. They are used by individual freelancers, large publishing houses, and entire marketing departments. The algorithm writes quickly, smoothly, without a hint of emotion or fatigue. But this apparent perfection hides a major pitfall.
The crux of the problem is that the results of AI work are increasingly being published without any verification. In practice, the consequences are almost always the same. The material contains inaccuracies or half-truths that sound convincing but fall apart when the facts are examined in detail. The wording becomes unnaturally smooth and predictable. It seems correct, but there is no spark of living thought in it. And then reader trust falls, SEO optimization suffers, and the reputation of the brand or publication suffers.
That is why modern editorial and content departments in the West are already talking about introducing a special stage—AI review—a return to the fundamental rule of journalism: any material, regardless of who created it, human or machine, must be reviewed before publication.
At this stage, many editors make a critical mistake — they limit themselves to a single review tool and consider the task complete. But the AI market is developing too quickly, and recognition methods are also evolving. To understand what signs really give away machine-generated text and what search engines and editorial offices are paying attention to today, it is important to be familiar with the current analysis tools and their limitations. A detailed analysis of such solutions allows you to build a more reliable AI review and not rely on blind automation.
Detectors are not judges, but signal flags
When it comes to checking text from a neural network, many people's first idea is: “Let's run it through a detector, and everything will become clear.” The desire is understandable, but this is often where mistakes begin.
All reputable sources agree on one thing: detectors do not give “yes” or “no” verdicts. They only calculate probabilities, look for patterns, and work with statistics.
So it is a mistake to perceive such a tool as a judge. Its real role is to be a filter or a red flag. It will not answer whether the text can be published. It will point out areas that require special attention from a live editor.
Experts strongly advise against trusting a single service. One detector may show a low percentage of “artificiality,” while another may show an off-the-charts percentage. This does not mean that someone is lying. It simply means that their analysis algorithms are different. Practitioners recommend checking the text in two or three systems and looking not at bare percentages, but at consistent patterns.
The main value of such a check is not even the final figure, but the paragraphs that different services consistently mark as problematic. This is where the typical weaknesses of machine-generated text usually lie: overly polished wording, clichéd phrases, and bare generalizations without details. For an editor, this is not a death sentence for the material, but a clear signal that manual work is needed here.
In the AI-review checklist, detectors play an important but auxiliary role. They cannot replace humans or make decisions for them. Their task is to help quickly find areas where “just normal” text can be made truly high-quality — human, accurate, and suitable for publication.
Where the algorithm most often “gives itself away”
If we distract ourselves from detectors, AI most often gives itself away not with its vocabulary, but with the structure of the text. Observations by leading experts confirm this: neural networks write with unnatural accuracy. And this exaggerated correctness begins to hurt the eyes.
A typical AI text looks as if it has already been edited before it was written. The paragraphs are almost identical in length. Thoughts flow smoothly, without the slightest pause or hesitation. Everything is logical, consistent, and... lifeless.
Another characteristic feature is universal clichés that can be inserted into an article on any topic. Phrases such as “It is important to note that...” or “Thus, we can conclude...” are not a sin in themselves. But when they wander from text to text without carrying any unique meaning, it is almost a sure sign of machine origin.
For the reader, such predictability kills interest. For the editor, it is a clear warning sign: the text is too smooth, too correct, and too safe.
Therefore, the next checkpoint is a targeted search for this very “perfection.” Are the paragraphs too symmetrical? Can the formal introductions and conclusions be cleaned up or rewritten “for the sake of form”? Are there lively connections, clarifications, and unique details in the text, rather than generalities?
The editor's job here often boils down to simple things: cutting out template constructions, breaking the monotonous rhythm of paragraphs, allowing the text to breathe and be uneven. In real journalism, thoughts are rarely arranged in a flawless parade. It is this slight chaos that makes the material lively and convincing.
How to work with facts and statements
While the style and structure of AI text can still be dealt with through editing, the factual side of things is more serious. This is where neural networks make mistakes most often, and the consequences of these mistakes are the most serious.
AI can speak with frightening confidence. It can present a dubious or simply fabricated statement in such a way that it sounds like an irrefutable truth. This is especially true for numbers, historical dates, and logical connections between events. The algorithm can round off statistics, mix data from different studies, or “think up” a cause-and-effect relationship that has not been proven.
The result is a paradox: the text sounds convincing, but is far from the truth. And this is the most dangerous part. The reader will not suspect anything until they check the information themselves. And for the author, media outlet, or brand, such an oversight can become a reputational disaster.
That is why SEO specialists insist on one simple rule: any fact in a text generated by AI is considered unverified by default. In other words, manual verification of all specific data in the text is vitally important. Search for original research, official reports, and authoritative publications. And if the source cannot be found, the editor must do the only honest thing: either soften the wording by adding “possibly” or “according to some data,” or remove the controversial point from the text altogether.
How SEO optimization works
AI is often presented as the ideal SEO assistant. It knows everything about keywords, can build structure, and instantly generates texts for queries. But without editing, the algorithm can easily turn from an assistant into a source of problems for search engine optimization.
The most common problem is oversaturation. AI tries to please and generously sprinkles keywords throughout the text. The result is material that search engines can evaluate, but which is unbearable to read. The second problem is formal headlines. They contain the right words, but they are not catchy and have no value. The third is ignoring the user's true intention. The text seems to respond to the query, but does not provide a specific, useful answer.
Here it is important to remember the principle that experts insist on. Google and other systems have long been evaluating not technical perfection, but quality, usefulness, and compliance with the expectations of a living person. If the text does not solve the reader's problem, no optimization will save it.
Therefore, SEO verification as part of an AI review is about meaning. In the checklist, it looks like this:
- Review the headlines: are they catchy and useful, or do they just contain keywords?
- Ask yourself: does the material really answer the question that the user came with?
- Ruthlessly clean out SEO fluff, meaningless repetitions, and phrases that exist only for robots.
At this stage, the editor must put themselves in the reader's shoes. Would they themselves answer their own question after reading this text? Would they want to read to the end? If there is even a shadow of doubt, then the algorithm has only done half the job, and there is still work to be done.
Where does the responsibility lie?
Artificial intelligence is not the enemy. The threat lies elsewhere—in the disappearance of a living person between the moment the text is generated and its publication.
Neural networks help overcome the fear of a blank page, sketch out a structure, and find the first formulations. But they do not and cannot bear responsibility for the truthfulness, depth, and consequences of the published word. This responsibility has been and remains with authors, editors, companies, and publishers.
Many people perceive neural networks exclusively as text generators, but in practice, AI has long been used much more widely — from analytics to creativity and content scaling. Understanding real-world scenarios for using neural networks allows you to build processes so that algorithms enhance the result rather than creating hidden risks for reputation and SEO.
