By Cheval John | Vallano Media
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
One of the least useful things artificial intelligence can do for a journalist is agree with everything.
That may sound strange.
Most people want tools that help them.
They want answers.
They want suggestions.
They want reassurance that the work is good.
But journalism does not improve simply because someone — or something — says:
“This looks great.”
Sometimes the most valuable response is:
“This argument does not hold up.”
That realization changed the way I use AI.
Instead of asking only for help producing the work, I began asking AI to challenge the work.
Attack the argument.
Find the weakness.
Question the assumption.
Show me what I may have overlooked.
That became one of the most useful parts of my AI-assisted editorial process.
I call it red-teaming.
In this book, I am using that term in an editorial sense: deliberately stress-testing the claims, reasoning, sourcing and framing of a story before publication.
Agreement Is Easy
AI systems are designed to be helpful.
That can become a problem.
If I give an AI system an argument and ask:
“Does this make sense?”
I may receive a supportive response explaining why the argument works.
If I ask:
“Is this a strong article?”
I may hear that the article is thoughtful, compelling or well structured.
That may feel good.
It may also tell me very little.
The problem is not that positive feedback is always wrong.
The problem is that agreement does not test the work.
Journalism needs resistance.
A source may challenge the premise.
An editor may question the evidence.
A reader may notice an assumption the writer missed.
If I want the article to survive those challenges after publication, it makes sense to expose it to some resistance before publication.
AI can sometimes help simulate that kind of critique.
It is not the same as an experienced editor or a knowledgeable subject-matter expert.
But it can provide another angle from which to examine the work.
Ask the Opposite Question
One of the simplest changes I made was changing the way I ask AI for feedback.
Instead of:
“Is this argument strong?”
I can ask:
“What is the strongest evidence-based challenge to this argument?”
Instead of:
“Does this article make sense?”
I can ask:
“Where could a skeptical reader say this article fails?”
Instead of:
“Is this ready to publish?”
I can ask:
“What weakness could justify holding this story?”
The difference is significant.
The first set of questions invites approval.
The second invites resistance.
That resistance can expose problems I might not notice because I am already too close to the work.
Writers Become Attached to Their Arguments
This is not an AI problem.
It is a human problem.
Once I have spent time researching and writing something, I naturally begin to understand the story through the structure I created.
I know what I meant.
I know why I included a particular detail.
I know how the argument developed.
That familiarity can create blind spots.
A paragraph may seem clear to me because I already know the background.
A conclusion may feel obvious because I have been thinking about it for hours.
A source may seem convincing because it supports the direction the story has already taken.
That is exactly when another perspective becomes useful.
Editors have always served that role.
Good editors ask the question the writer may not want to hear.
Why?
How do you know?
Where is the evidence?
Is that really what the source said?
Are you assuming too much?
What might an informed critic object to?
AI can sometimes help simulate that resistance.
Not replace the editor.
Not replace the journalist.
But add another layer of challenge before publication.
What Red-Teaming Means in My Workflow
When I red-team an article, I am not simply asking AI to proofread it.
Proofreading looks for problems in grammar, spelling and presentation.
Red-teaming asks whether the underlying journalism survives criticism.
The questions are different.
What is the weakest claim?
Where is the evidence thin?
Which sentence sounds more certain than the reporting supports?
What assumption am I making?
Is there another plausible interpretation?
What evidence-based weakness might a knowledgeable reader identify?
Have I confused correlation with causation?
Am I treating an opinion as a fact?
Does the headline promise more than the article proves?
Is there missing context that would materially change the reader’s understanding?
Those questions force the work into a more uncomfortable place.
That is the point.
Do Not Manufacture an Opposing Side
There is a danger in telling AI simply:
“Argue the other side.”
The system will usually try.
But the existence of a counterargument does not make the counterargument credible.
It may create a false equivalence.
It may elevate a fringe objection.
It may invent controversy where the evidence is actually quite strong.
That is why I prefer a narrower instruction.
Do not merely give me an opposing opinion.
Show me “evidence-based weaknesses, missing context, unsupported inferences and plausible alternative interpretations.”
That is a much better editorial test.
The question is not:
“Can somebody disagree with this?”
Someone almost always can.
The better question is:
“Does the disagreement expose a legitimate weakness in the evidence or reasoning?”
AI Can Be Wrong About the Weakness Too
There is another important warning.
Red-teaming does not mean accepting every criticism AI produces.
AI can identify a problem that is not actually a problem.
It can misunderstand the argument.
It can invent a counterargument that has little connection to the evidence.
It can overcorrect.
It can treat a well-supported conclusion as questionable simply because I instructed it to find something wrong.
That means the red-team response has to be evaluated too.
This connects directly back to the earlier chapters.
The human remains responsible.
AI output is not evidence.
The human has to exercise judgment.
Red-teaming gives me another perspective.
It does not give AI veto power over the article.
The Goal Is Not to Destroy the Story
A red-team process can go too far.
If I challenge every sentence endlessly, nothing will ever be published.
Every story has limitations.
Every argument can be questioned.
Every source can be criticized.
The goal is not to produce journalism that no human being could possibly challenge.
That is impossible.
The goal is to discover weaknesses that matter.
A missing comma is not the same as a missing source.
A stylistic disagreement is not the same as an unsupported accusation.
A theoretical alternative explanation is not automatically as credible as the explanation supported by the available evidence.
Red-teaming is useful only if the journalist can distinguish criticism generated for the sake of criticism from a material weakness.
Focus on What Could Change the Story
AI can produce long lists.
That does not mean every item deserves equal attention.
If a red-team review gives me ten objections, I do not automatically rewrite the story ten times.
I want to know:
Which objection could change the factual conclusion?
Which one exposes weak sourcing?
Which one reveals missing context or distorted framing?
Which one could damage trust if I ignore it?
That is a more useful filter.
One serious weakness is worth more than ten superficial criticisms.
AI can generate objections.
The journalist has to rank them.
Surviving a Red-Team Pass Does Not Prove the Story Is Correct
This distinction matters.
If a story survives an AI red-team review, that does not certify the journalism.
The AI may have missed something.
It may share the same assumptions that influenced the original work.
Its critique may simply be weak.
Passing one adversarial review means only that **one additional layer of challenge did not reveal a material problem**.
That is useful.
It is not proof.
Verification still matters.
Human editing still matters.
The underlying reporting still matters.
Red-teaming is another safeguard.
It is not a replacement for the others.
The Same AI Is Not an Independent Editor
There is another limitation worth acknowledging.
Sometimes the same AI system that helped with the draft also performs the red-team review.
That can still be useful.
But it is not independent review.
The model may carry forward assumptions from the earlier discussion.
It may understand the article through the framing already established in the conversation.
It may reproduce the same blind spots.
The higher the stakes, the less comfortable I should be relying on one system to create the work and then declare that it survived criticism.
Outside human review may be necessary.
Additional sourcing may be necessary.
A different evaluation approach may be necessary.
The AI red team is one perspective.
It should not pretend to be every perspective.
Red-Teaming Can Improve Reporting, Not Just Writing
One of the most useful things about the process is that it can send me back to the reporting.
Maybe the problem cannot be fixed by rewriting a sentence.
Maybe I need another source.
Maybe I need the original document.
Maybe I need to check the timeline again.
Maybe I need to separate what is confirmed from what I believe happened.
That is when red-teaming becomes more than an editing exercise.
It becomes part of the reporting process.
A good challenge can reveal that the story is not finished yet.
And that is valuable information to receive before publication.
Challenge the Headline Too
Headlines deserve their own red-team review.
A headline can be technically related to the article and still overstate what the reporting establishes.
That is especially tempting in digital publishing.
Strong language attracts attention.
Certainty attracts clicks.
Conflict attracts attention.
But a headline is still part of the journalism.
If the headline is stronger than the evidence, the article begins with a credibility problem.
So one of the questions worth asking is:
“Does the headline make a claim the article cannot fully support?”
That simple test can prevent a lot of unnecessary trouble.
Red-Team the AI Too
There is another layer to this process.
The AI itself should be challenged.
If AI summarizes a source, I can ask:
What did you omit?
What uncertainty did you flatten?
Show me where the source supports that statement.
What alternative interpretation exists?
If AI suggests a conclusion, I can ask:
“What evidence would make this conclusion wrong?”
If AI gives me a confident answer, I can ask:
“What part of this answer requires the most verification?”
The goal is not to create an endless conversation with the machine.
The goal is to prevent confidence from becoming authority.
AI should not merely challenge my work.
I should challenge its work too.
A Strong Review Also Identifies What Survives
Red-teaming should not become an exercise in finding fault for its own sake.
A useful review should also identify which claims remain strong after scrutiny.
What is well supported?
Which conclusion survived the challenge?
Which source still holds up?
Which section does not need to be rewritten?
That matters because the purpose of red-teaming is diagnosis.
Not destruction.
A good review separates the parts of the story that need more work from the parts that have already earned their place.
The Psychological Value of an Adversarial Partner
There is another benefit to red-teaming that is harder to measure.
It makes criticism normal.
Instead of treating criticism as something that arrives after publication, I can make it part of the production process.
That changes my relationship with the draft.
The goal becomes less about defending what I wrote and more about improving it.
If the red team finds something important, good.
Better now than after publication.
If the argument survives, good.
I have more confidence in the process because I tried to break it.
Not because the red-team pass proved the story correct.
It did not.
But because the work has faced another layer of scrutiny.
A Workflow for Challenging the Work
A journalist does not need an elaborate system to begin.
A simple editorial red-team workflow can look like this:
Finish the draft.
Identify the central claim.
Ask for the strongest evidence-based challenges.
Look for weak or overstated claims, missing context and unsupported inferences.
Identify which weaknesses could materially change the story.
Then evaluate every criticism yourself.
Some will be useful.
Some will be irrelevant.
Some will be wrong.
The human decides which ones require action.
That may lead to a rewrite.
It may lead to more reporting.
It may lead to stronger sourcing.
Or it may confirm that the original argument already has strong support.
All of those outcomes can be useful.
Challenge Before the Audience Does
Every published story eventually meets readers who did not participate in creating it.
They bring different experiences.
Different knowledge.
Different assumptions.
Different questions.
Some will notice things the writer missed.
That cannot be eliminated.
But journalism can become stronger when the writer deliberately invites challenge before publication.
AI gives me another way to do that.
Not because AI knows better than the journalist.
Not because its criticism is automatically correct.
And not because surviving an AI challenge proves the journalism is right.
The value comes from resistance.
Do not use AI only to help build the argument.
Use it to pressure-test the argument too.
Then decide what survives.
Because a story that has been seriously challenged before publication has a better chance of surviving the challenges that come after it.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
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Make AI Challenge the Story
By Cheval John | Vallano Media
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
One of the least useful things artificial intelligence can do for a journalist is agree with everything.
That may sound strange.
Most people want tools that help them.
They want answers.
They want suggestions.
They want reassurance that the work is good.
But journalism does not improve simply because someone — or something — says:
“This looks great.”
Sometimes the most valuable response is:
“This argument does not hold up.”
That realization changed the way I use AI.
Instead of asking only for help producing the work, I began asking AI to challenge the work.
Attack the argument.
Find the weakness.
Question the assumption.
Show me what I may have overlooked.
That became one of the most useful parts of my AI-assisted editorial process.
I call it red-teaming.
In this book, I am using that term in an editorial sense: deliberately stress-testing the claims, reasoning, sourcing and framing of a story before publication.
Agreement Is Easy
AI systems are designed to be helpful.
That can become a problem.
If I give an AI system an argument and ask:
“Does this make sense?”
I may receive a supportive response explaining why the argument works.
If I ask:
“Is this a strong article?”
I may hear that the article is thoughtful, compelling or well structured.
That may feel good.
It may also tell me very little.
The problem is not that positive feedback is always wrong.
The problem is that agreement does not test the work.
Journalism needs resistance.
A source may challenge the premise.
An editor may question the evidence.
A reader may notice an assumption the writer missed.
If I want the article to survive those challenges after publication, it makes sense to expose it to some resistance before publication.
AI can sometimes help simulate that kind of critique.
It is not the same as an experienced editor or a knowledgeable subject-matter expert.
But it can provide another angle from which to examine the work.
Ask the Opposite Question
One of the simplest changes I made was changing the way I ask AI for feedback.
Instead of:
“Is this argument strong?”
I can ask:
“What is the strongest evidence-based challenge to this argument?”
Instead of:
“Does this article make sense?”
I can ask:
“Where could a skeptical reader say this article fails?”
Instead of:
“Is this ready to publish?”
I can ask:
“What weakness could justify holding this story?”
The difference is significant.
The first set of questions invites approval.
The second invites resistance.
That resistance can expose problems I might not notice because I am already too close to the work.
Writers Become Attached to Their Arguments
This is not an AI problem.
It is a human problem.
Once I have spent time researching and writing something, I naturally begin to understand the story through the structure I created.
I know what I meant.
I know why I included a particular detail.
I know how the argument developed.
That familiarity can create blind spots.
A paragraph may seem clear to me because I already know the background.
A conclusion may feel obvious because I have been thinking about it for hours.
A source may seem convincing because it supports the direction the story has already taken.
That is exactly when another perspective becomes useful.
Editors have always served that role.
Good editors ask the question the writer may not want to hear.
AI can sometimes help simulate that resistance.
Not replace the editor.
Not replace the journalist.
But add another layer of challenge before publication.
What Red-Teaming Means in My Workflow
When I red-team an article, I am not simply asking AI to proofread it.
Proofreading looks for problems in grammar, spelling and presentation.
Red-teaming asks whether the underlying journalism survives criticism.
The questions are different.
Those questions force the work into a more uncomfortable place.
That is the point.
Do Not Manufacture an Opposing Side
There is a danger in telling AI simply:
“Argue the other side.”
The system will usually try.
But the existence of a counterargument does not make the counterargument credible.
It may create a false equivalence.
It may elevate a fringe objection.
It may invent controversy where the evidence is actually quite strong.
That is why I prefer a narrower instruction.
Do not merely give me an opposing opinion.
Show me “evidence-based weaknesses, missing context, unsupported inferences and plausible alternative interpretations.”
That is a much better editorial test.
The question is not:
“Can somebody disagree with this?”
Someone almost always can.
The better question is:
“Does the disagreement expose a legitimate weakness in the evidence or reasoning?”
AI Can Be Wrong About the Weakness Too
There is another important warning.
Red-teaming does not mean accepting every criticism AI produces.
AI can identify a problem that is not actually a problem.
It can misunderstand the argument.
It can invent a counterargument that has little connection to the evidence.
It can overcorrect.
It can treat a well-supported conclusion as questionable simply because I instructed it to find something wrong.
That means the red-team response has to be evaluated too.
This connects directly back to the earlier chapters.
The human remains responsible.
AI output is not evidence.
The human has to exercise judgment.
Red-teaming gives me another perspective.
It does not give AI veto power over the article.
The Goal Is Not to Destroy the Story
A red-team process can go too far.
If I challenge every sentence endlessly, nothing will ever be published.
Every story has limitations.
Every argument can be questioned.
Every source can be criticized.
The goal is not to produce journalism that no human being could possibly challenge.
That is impossible.
The goal is to discover weaknesses that matter.
A missing comma is not the same as a missing source.
A stylistic disagreement is not the same as an unsupported accusation.
A theoretical alternative explanation is not automatically as credible as the explanation supported by the available evidence.
Red-teaming is useful only if the journalist can distinguish criticism generated for the sake of criticism from a material weakness.
Focus on What Could Change the Story
AI can produce long lists.
That does not mean every item deserves equal attention.
If a red-team review gives me ten objections, I do not automatically rewrite the story ten times.
I want to know:
That is a more useful filter.
One serious weakness is worth more than ten superficial criticisms.
AI can generate objections.
The journalist has to rank them.
Surviving a Red-Team Pass Does Not Prove the Story Is Correct
This distinction matters.
If a story survives an AI red-team review, that does not certify the journalism.
The AI may have missed something.
It may share the same assumptions that influenced the original work.
Its critique may simply be weak.
Passing one adversarial review means only that **one additional layer of challenge did not reveal a material problem**.
That is useful.
It is not proof.
Verification still matters.
Human editing still matters.
The underlying reporting still matters.
Red-teaming is another safeguard.
It is not a replacement for the others.
The Same AI Is Not an Independent Editor
There is another limitation worth acknowledging.
Sometimes the same AI system that helped with the draft also performs the red-team review.
That can still be useful.
But it is not independent review.
The model may carry forward assumptions from the earlier discussion.
It may understand the article through the framing already established in the conversation.
It may reproduce the same blind spots.
The higher the stakes, the less comfortable I should be relying on one system to create the work and then declare that it survived criticism.
Outside human review may be necessary.
Additional sourcing may be necessary.
A different evaluation approach may be necessary.
The AI red team is one perspective.
It should not pretend to be every perspective.
Red-Teaming Can Improve Reporting, Not Just Writing
One of the most useful things about the process is that it can send me back to the reporting.
Maybe the problem cannot be fixed by rewriting a sentence.
Maybe I need another source.
Maybe I need the original document.
Maybe I need to check the timeline again.
Maybe I need to separate what is confirmed from what I believe happened.
That is when red-teaming becomes more than an editing exercise.
It becomes part of the reporting process.
A good challenge can reveal that the story is not finished yet.
And that is valuable information to receive before publication.
Challenge the Headline Too
Headlines deserve their own red-team review.
A headline can be technically related to the article and still overstate what the reporting establishes.
That is especially tempting in digital publishing.
Strong language attracts attention.
Certainty attracts clicks.
Conflict attracts attention.
But a headline is still part of the journalism.
If the headline is stronger than the evidence, the article begins with a credibility problem.
So one of the questions worth asking is:
“Does the headline make a claim the article cannot fully support?”
That simple test can prevent a lot of unnecessary trouble.
Red-Team the AI Too
There is another layer to this process.
The AI itself should be challenged.
If AI summarizes a source, I can ask:
If AI suggests a conclusion, I can ask:
“What evidence would make this conclusion wrong?”
If AI gives me a confident answer, I can ask:
“What part of this answer requires the most verification?”
The goal is not to create an endless conversation with the machine.
The goal is to prevent confidence from becoming authority.
AI should not merely challenge my work.
I should challenge its work too.
A Strong Review Also Identifies What Survives
Red-teaming should not become an exercise in finding fault for its own sake.
A useful review should also identify which claims remain strong after scrutiny.
What is well supported?
Which conclusion survived the challenge?
Which source still holds up?
Which section does not need to be rewritten?
That matters because the purpose of red-teaming is diagnosis.
Not destruction.
A good review separates the parts of the story that need more work from the parts that have already earned their place.
The Psychological Value of an Adversarial Partner
There is another benefit to red-teaming that is harder to measure.
It makes criticism normal.
Instead of treating criticism as something that arrives after publication, I can make it part of the production process.
That changes my relationship with the draft.
The goal becomes less about defending what I wrote and more about improving it.
If the red team finds something important, good.
Better now than after publication.
If the argument survives, good.
I have more confidence in the process because I tried to break it.
Not because the red-team pass proved the story correct.
It did not.
But because the work has faced another layer of scrutiny.
A Workflow for Challenging the Work
A journalist does not need an elaborate system to begin.
A simple editorial red-team workflow can look like this:
Finish the draft.
Identify the central claim.
Ask for the strongest evidence-based challenges.
Look for weak or overstated claims, missing context and unsupported inferences.
Identify which weaknesses could materially change the story.
Then evaluate every criticism yourself.
Some will be useful.
Some will be irrelevant.
Some will be wrong.
The human decides which ones require action.
That may lead to a rewrite.
It may lead to more reporting.
It may lead to stronger sourcing.
Or it may confirm that the original argument already has strong support.
All of those outcomes can be useful.
Challenge Before the Audience Does
Every published story eventually meets readers who did not participate in creating it.
They bring different experiences.
Different knowledge.
Different assumptions.
Different questions.
Some will notice things the writer missed.
That cannot be eliminated.
But journalism can become stronger when the writer deliberately invites challenge before publication.
AI gives me another way to do that.
Not because AI knows better than the journalist.
Not because its criticism is automatically correct.
And not because surviving an AI challenge proves the journalism is right.
The value comes from resistance.
Do not use AI only to help build the argument.
Use it to pressure-test the argument too.
Then decide what survives.
Because a story that has been seriously challenged before publication has a better chance of surviving the challenges that come after it.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
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Tags: Adversarial Review, AI and Journalism, AI Bias, AI Hallucinations, AI Workflows, AI-assisted journalism, Artificial Intelligence, Digital Publishing, Editorial judgment, Editorial Red-Teaming, editorial workflow, Evidence-Based Journalism, fact-checking, False Balance, Headline Review, Human oversight, Human-AI Collaboration, Human-in-the-Loop, Journalism Ethics, Journalism Standards, Journalist and AI, Media Trust, Newsroom AI, Red Teaming, Responsible AI, Source Verification, verification
About Cheval John
Cheval serves as Managing Director of Vallano Media, LLC, where he oversees company's editorial strategy, business development and digital media operations. Operating at the intersection of social media management and sports, Vallano Media is building a dual-pillar brand that does two things: deliver real growth for businesses and translate the sports strategies into practical business lessons. Cheval hosted "What's The Word?" a podcast about finding out what inspires people to choose their respective careers and how social media impacted their lives and business from 2013 to 2017. He has spoken at Social Media Week Lima in Ohio and at Social Media Day Houston 2017. He was named a Houston Top 25 Social Media Power Influencer (2016 and 2017) and Twitter (Now X)Top 50 Influencer by Onalytica in 2018.