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Four AI Mistakes That Are Making Your Content Generic
When AI Weakens Professional Writing
In this video, I analyze how the misuse of artificial intelligence in professional content creation produces generic, predictable writing, presenting four recurring mistakes and showing how to correct them in practice through clear criteria, logical structure, and deliberate control of the tool.
Audio Experience
Listen to the complete article in audio, preserving its original structure, pacing, and intent.
How to Keep AI from Standardizing Your Writing
I decided to create this episode because I noticed a clear pattern on LinkedIn over the past few months.
The feed is filled with long, organized, technically correct, and visually similar texts.
They convey a sense of competence, but rarely a sense of human identity.
The central problem is the homogenization of language.
Artificial intelligence accelerated this phenomenon by replicating structures, cadences, and formal choices that had already become established as acceptable patterns.
The goal here is not to teach you how to use artificial intelligence, but to establish criteria for using it while preserving voice, clarity, and intellectual authority.
Below, I organize the episode’s complete reasoning in written form, expanding each mistake, explaining the structural mechanism behind it, and detailing the practical step-by-step correction inside the tool.
If you are just getting started with the topic or want to review the fundamentals of prompt engineering, I published an introductory article that organizes the concepts, examples, and best practices progressively. It provides the theoretical foundation for what we explore here in greater depth.
LinkedIn Saturation and the Role of Artificial Intelligence
LinkedIn has entered a stage of structural predictability.
Texts follow the same logic of opening, development, and conclusion.
Ideas are presented in clearly separated blocks, with recurring visual markers and arguments organized around simplified contrasts.
We created this pattern, and AI began amplifying it.
AI tools operate by recognizing and reproducing dominant patterns.
When an environment already rewards certain formats, the machine learns quickly and reproduces them at scale.
Without clear constraints, it delivers exactly what statistically works, even when that comes at the cost of identity and intellectual depth.
The central point of this content is to establish criteria.
Criteria for form, structure, output, and intellectual responsibility.
Mistake #1: Visual Writing Habits and Excessive Use of Dashes ( - )
The first mistake is easy to spot.
Texts packed with dashes, slashes, graphic separators, and fragmented lists.
The reader recognizes the visual pattern before processing the content.
That automatic recognition creates silent rejection.
The text receives less attention even when the idea itself is relevant.
This problem is about visual consistency and reading flow.
Artificial intelligence uses graphic markers to organize ideas quickly and generically.
That behavior reduces the machine’s effort but transfers the effort to the reader.
Practical Correction
The adjustment is made in the tool’s persistent instructions.
I state explicitly that I do not use dashes, slashes, or repetitive graphic structures.
Step by step:
1. Open the tool’s persistent-instructions area.
State directly that the text should avoid dashes, slashes, and recurring visual markers.
Run the same request before and after adding the instruction to validate the effect.
I used this prompt for the before-and-after comparison:
From this instruction onward, every generated text must completely exclude the use of dashes, forward slashes, and recurring visual markers. These symbols should not be treated as available grammatical devices, nor used to explain, separate ideas, or create visual rhythm.
The test takes less than twenty seconds.
The difference in the text’s visual rhythm appears immediately.
Compare the before and after:
Mistake #2: Repetitive Semantic Structure and Predictable Argumentation
The second mistake lies in the architecture of the reasoning.
Artificial intelligence tends to respond using repeated semantic formulas.
Broad introduction, explanation through simplified contrast, conciliatory conclusion.
That structure repeats regardless of the topic.
What creates predictability is the argumentative structure.
When readers identify the pattern too early, they anticipate the text and lose interest.
Practical Correction
The correction comes through structural blocking. Many people think they need to add many behavioral instructions here, but that is not quite right. We will work on creativity requests later; filters always come first.
I make it clear in the persistent instructions that I do not accept repeated rhetorical structures or artificial contrast formulas.
I ask for declarative, cumulative responses built through logical progression.
Step by step
Add a restriction against standardized rhetorical structures to the persistent instructions.
Ask for development through logical progression, without artificial opposition between ideas.
Test the same prompt before and after the instruction.
I like to use this prompt:
From this instruction onward, completely avoid semantic constructions based on rhetorical contrast, binary opposition, or negation followed by conceptual replacement. Do not use structures such as “it is not X, it is Y,” “we are not talking about A, but B,” “it was not merely something simple, it was something bigger,” or equivalent variations.
Evaluate whether the structure changed.
Proper validation occurs when the text begins to develop through progressive depth. Predictable structure tires the audience before the content is absorbed.
Mistake #3: Long Prompt, Poorly Structured Idea
There is a common misunderstanding here.
People treat AI as though each conversation were an isolated request, when in practice it performs much better inside a previously defined operating pattern.
The problem is asking for execution before establishing filters and behavior.
What I do is completely separate those stages.
I treat behavior definition as an independent project that comes before any execution.
Practical Correction
Before asking for any deliverable, I make the AI behave the way I expect.
Step by step
First, I use artificial intelligence itself to help me structure a behavior prompt.
I explicitly ask how to create an instruction focused on the type of writing, type of reasoning, level of depth, and limits I want to impose.
At this point, I am designing rules.
I define filters before defining output.
I am establishing what can and cannot happen inside that environment.
After that, I open a new project.
Once the project is created, I place these instructions in the tool’s personalization area.
This space is used to define the operating contract.
That is exactly where I describe how the AI should behave consistently and, most importantly, which behaviors should be avoided.
From that point on, every interaction begins from a cleaner, more controlled pattern that is closer to the way I think.
The tool stops improvising and begins operating within a clear set of constraints.
Mistake #4: Lack of Output Criteria
The fourth mistake is accepting any long answer as a good answer.
When format, length, tone, and constraints are not defined, artificial intelligence produces excess.
Practical Correction
Control happens before generation.
I describe the response before it exists.
Step by step
Define the desired format, for example continuous prose for LinkedIn.
Set an approximate length limit.
State quality criteria such as clarity, absence of clichés, and absence of structural repetition.
Explicitly indicate what must not appear in the text.
When you compare a response with and without a defined format, the difference is obvious. The output becomes more objective, denser, and more usable.
Essential Highlights
Key excerpts from the original content, designed for quick reading without losing context.
First the Filter, Then the Behavior
When I applied these changes in a structured way, the result changed noticeably.
The difference showed up in the tool’s own behavior across interactions.
The responses became more restrained, more predictable in a good way, and closer to how I reason and write.
That happens because the AI stops reacting to isolated prompts and begins operating within a system of clear constraints.
The key is understanding that AI works with layers of memory, and each layer serves a different function.
When those layers are used without awareness, the tool mixes patterns, improvises structures, and reproduces dominant formulas.
When they are organized correctly, it becomes much more precise.
The first layer is general memory.
This is where I do the cleanup.
In this memory, I define everything that should never be part of any response, regardless of context.
Dashes, slashes, repetitive visual markers, artificial rhetorical contrasts, and predictable argumentative structures are blocked at this level.
This layer works as a permanent filter.
Everything the AI produces already passes through this initial screen.
The goal here is not to direct creative behavior, but to remove structural noise and automatic habits.
Only after that cleanup does it make sense to move to the second layer: project memory.
This memory is used to provide direction.
It is where I describe how the artificial intelligence should behave within that specific context.
Type of writing, type of reasoning, level of depth, editorial boundaries, and quality criteria.
This layer functions as an operating contract.
The AI stops improvising because it already knows the rules of the game.
The order of these stages is decisive.
When behavior is defined before cleanup, the tool continues carrying unwanted patterns underneath the instructions.
When cleanup comes first, the intended behavior appears much more faithfully.
This is the point at which it stops being a generic text generator and starts functioning as a professional tool for supporting thinking and execution.
Key Takeaways
Core ideas that expand and deepen this analysis.
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