How To Fact Check AI Generated Content In 7 Steps

To fact check AI generated content, verify every checkable claim against its original source before you publish: cross-reference facts across multiple credible sources, establish who published each source and what credentials they hold, assess whether a claim is plausible in context, check publication dates, cite the original study rather than an article about it, separate stated facts from opinion, and apply stricter scrutiny to YMYL topics. AI writing tools produce fluent text, not verified text, so a human still has to confirm the details. The seven steps below walk through that process in order, plus what to look for in a source before you start.

Why is it important to fact check AI content?

Fact checking matters because AI generated content is fluent by design and accurate only by accident. A language model predicts plausible wording; it does not know whether the claim it just produced is true, and it cannot vouch for the quality of the material it learned from. Publishing an unchecked draft puts your name behind whatever the model got wrong, and one invented statistic or misattributed quote costs you reader trust, search visibility, and revenue. Your reputation as a writer or company is everything, so treat every AI draft as exactly that: a draft that a person still has to verify line by line.

There are two fundamental reasons why you need to fact check content generated by an AI writing assistant:

Reputation

This one is a biggie because your reputation as a writer or company is everything, and publishing media content that misleads or is false or fake news on social media platforms or websites is a no-no.

A bad reputation costs you opportunities and money, which directly affects your bottom line for the worse. So it's imperative to use a proper fact checking process to ensure that the content you publish and share is 100% accurate.

Imagine the consequences if as a student, your essays are flagged to come from essay writing apps.

AI is as good as its source information

AI writers or tools like Surfer AI are trained or programmed on data (most of which is available online) to create content.

The first problem is that the training data or source data may not be correct. Current models are no longer stuck with whatever they were trained on: OpenAI's model documentation lists web search among the tools its models can call, so an assistant can look a claim up and hand back the pages it used. What the model still doesn't do is confirm that the page it found actually supports the sentence it wrote, and that part is on you.

The second issue is that AI is artificial, created by fallible humans, so AI systems can unknowingly adopt biases based on the information it uses.

Thus, AI copywriters can often create incorrect text and fictional content.

In its current form, this is one of AI generated content's greatest weaknesses. But you have a responsibility to publish valuable, helpful, and insightful information.

Look at this tweet by Daniel Dominguez, who highlights some of AI's limitations:

If your readers aren't staying on your website to read reliable, credible, and helpful content, it'll result in a drop in search rankings, which have far-reaching repercussions for your business.

Therefore, it's crucial to know the limitations of AI generated content and recognize the potential inaccuracies and biases when you use AI writing tools.

The size of the gap is measurable. Vectara's public hallucination leaderboard scores leading models on a deliberately easy task: summarize a supplied document using only the facts in that document. Checked on 12 August 2026, the leaderboard (last updated 11 May 2026) put the best model at a 1.8% hallucination rate, most models between roughly 3% and 15%, and the weakest entry at 24.2%. Free-form writing, where there is no source document to check the output against, gives a model far more room to invent. Those rates are much better than the first generation of AI writers managed, and they are still not zero.

And that's why human oversight and fact-checking of the AI created content is paramount and should not be skipped or underestimated.

What is AI hallucination?

An AI hallucination is when the AI writer, chatbot, or tool generates unexpected, weird, or fabricated content. The output reads like every other sentence the model produces: confident, fluent, correctly punctuated, and wrong. In practice it shows up as invented statistics, quotes nobody said, studies that do not exist, citations pointing at the wrong paper, and dates that are close but not right. Rates vary a lot by task and have fallen a long way since the first AI writing tools shipped, but no current model is hallucination free, and the mistakes are hardest to catch in exactly the places they do most damage: numbers, names, and sources.

Artificial intelligence doesn't reason about the world the way you do, and it's powered by a large language model (LLM) that learns patterns from vast amounts of data.

Because the AI writing tool can find patterns in the data, it predicts what words will follow in a sequence of words.

Newer models can be run in a reasoning mode that works through a problem in steps before answering, and OpenAI's model documentation lists selectable reasoning levels on its current models. Stepwise reasoning is not the same thing as verification, though.

The tool tries its utmost best to answer your prompt or search query, and even models that reason through a problem step by step can spit out inconsistencies, which is when the AI "hallucinates."

For example, CNET's adoption of AI generated text in November 2022, led to an article on compound interest containing multiple errors.

CNET had to correct the inaccuracies and basic facts that the AI got wrong, leaving them with some egg on their screens.

This is just one example of an AI hallucination; there are plenty more. The commonality of AI hallucinations further strengthens the case for human fact checking.

There is also a theoretical case that the problem never fully goes away. In "Hallucination is Inevitable: An Innate Limitation of Large Language Models", Ziwei Xu, Sanjay Jain and Mohan Kankanhalli argue that hallucination cannot be eliminated from LLMs. Their paper is a formal argument rather than a measurement: they define a world in which hallucination means a mismatch between a computable model and a computable ground truth, then use learning theory to show that a model cannot learn every computable function and so must get some answers wrong. Read it as a reason to keep a human in the loop, not as a prediction about any one tool.

What to look for when fact checking AI content

Before you check a single claim, work out how much weight the source behind it can carry. For every fact the AI produced, look for a named author with a byline, credentials that match the subject, the original data and the context it was first published in, the reason it was shared, any bias built into the study design, how recent it is, and who else has cited it. Those seven checks tell you whether a source is worth citing at all, and they are fast enough to run before you commit to using anything. Each one is covered below.

When you check the generative AI content, it's essential to pay attention to a few specifics, such as:

• Who wrote the source material:

‍Check if there are authors with bylines listed in the source material. A credible and trustworthy author will have a byline to state they wrote the article, but when there is no byline, you have to be extra aware because you don't know whether you can trust the information or where it came from.

With no byline (or a false one), the information could already be from a poorly checked AI source, which will cause your AI writer to generate further false content.

For example, all the content on our blog is accredited, and you can check out the author pages too. We'll even let you know when Surfer AI created an article, but you can rest assured that one of our editors has done their duty and checked the content.

• What credentials the author or website has:

‍Reliable sources will have various credentials to prove they are who they say they are and why they are authorities in their industry or niche.

You can check their social media accounts, about us page, author pages, and even the homepage for indications that the source materials' authors or websites are reputable sources.

• What does the original data say and in what context:

‍It's so easy to take a tidbit of information and use it without the right context. After all, this is what many sensational and fake news and media outlets use for clickbait.

So find the original source and the context in which it was said and published to ensure you post relevant and factual information.

• Why is the information being shared:

‍Misleading information is often shared for the wrong reasons, so ask yourself why the source material was compiled. Try to align your content by using information shared for a similar intent.

• Is the study or content biased in some way:

‍During the fact checking process, see if the information is objective and presented without bias.

Unfortunately, bias is inherent in all research designs, but researchers and writers (including AI) should take the necessary steps to limit bias.

• How current the information is:

‍Information from way back isn't always reliable because a lot may have changed since the research or publication date.

Experts recommend using up to date sources that date back no further than three to five years.

• Who else is citing the source:

‍The more people who cite the source, the more credibility is attached to it. However, it's critical to exercise caution and fact check the source and author. Not every piece of data that's been shared and cited over and over again is true.

For example, in 2022, Google dropped a "highly cited sources" label when you search for a topic on a mobile device, so you can use this to know what sources are cited often.

Stop joining the ranks of the sheeple who cite sources just for the sake of it; be the logical and reasoning person you are.

7 steps to fact check AI generated text 

Follow these 7 steps in your AI fact checking process to verify the accuracy of the information.

Cross-reference every claim against multiple credible sources, establish the credibility of each source you land on, assess whether the claim is plausible in its context, check the original publication date, cite the original study rather than an article reporting on it, separate stated facts from opinions and myths, and apply stricter scrutiny when the topic sits in the YMYL category, where Google's E-E-A-T expectations bite hardest. Work through the draft claim by claim rather than paragraph by paragraph, and highlight every fact that needs checking before you start.

Cross-reference information from multiple sources

You need to corroborate the information in the AI written content.

That means not just fact checking it from one source and marking it as good to go, but instead, cross-referencing it from various credible sources.

I'd suggest highlighting all the "facts" in the article first. In the content, check for any:

• Claims
• Statistics
• Conclusions
• Figures and numbers
• Names of people, events, and places
• Dates
• Quotes
• Historical claims
• News sources
• Other data

Then do a Google search for each "fact" and compare the information from multiple sources.

Only when you verify these facts through an authoritative and valuable source will you know that the info the AI tool wrote is correct and consistent.

An example of cross-referencing is seen in an authority site like Healthline, where the information is medically reviewed by an experienced fact checker with medical expertise and several medical studies for facts (some having multiple studies per fact).

Use fact checkers to verify facts and figures

You don't have to waste hours to fact check every claim the AI tool makes.

While you can use search engines like Google to verify information, rather save time, scale fact checking, and use automated fact checking tools.

For example, most AI assistants can now search the live web for a direct answer and hand back the pages they used. OpenAI documents this as a web search tool that gives a model access to up to date information and returns answers with sourced citations. That speeds up the hunt for a primary source, but it does not close the loop: open the cited page and confirm it says what the model claims it says.

Other fact checker options include Google Fact Check Explorer, Claimbuster, Microsoft's Natural Language Processing (NLP), and IBM Watson Discovery Service.

I explored the Google Fact Check Explorer, and while you can run a check on your own facts and statements (for free), you can also see recent fact searches. 

Establish credibility of the source

AI generated content (and well, all other articles) need to be checked so you can ensure the information comes from a reliable source.

At times, companies sponsor self-supporting studies, and often, the data will be skewed to showcase their service, product, or expertise in a better light to boost authority, credibility, trustworthiness, and sales.

This is standard practice across various industries, but it's better and safer for you (and your reputation) to fine-comb through the sources during content creation.

Don't cite questionable studies and content; rather, ensure that the content generated on the same topic is correct and helpful for your readers.

Another example of author verification is by medical sites like VeryWellMind, where the author's credentials are published along with their byline, with a popup that appears when you hover over their name.

When you follow the link, you arrive at  their author page.

Assess context and plausibility

When you use AI generated content, consider the context in which the content is presented by evaluating the plausibility of the statements or claims made. Also, cross check these against expert opinions, reputable research studies, and common knowledge.

You need to question and critically evaluate the content, whether you think you know or aren't sure.

Anything that pops as a red flag (such as sensational or controversial topics) should be marked as "possibly suspicious."

It's your responsibility to verify these red flags with supporting evidence, identify logical inconsistencies and contradictions, and analyze the content for coherence, consistency, and flow.

A human fact checker can stay up to date on the latest news in their SME industries, which will help them avoid publishing sensationalized or suspicious content as they are aware of the internal politics within their specific fields. 

For example, fact checking information on political topics may require that the fact checker be well-versed on the local politics and ethics before allowing content to be published.

Check the original date

Another critical element to fact check is the original date of publication.

Wherever possible, find a newer or more acceptable study or version of the article since using outdated sources may cause a few issues.

Information published "what feels like forever ago" may not be factually correct anymore because of technological, testing, and other advances in the field or industry.

Someone may also have discredited the original source, and some readers will think you are simply too lazy to find newer information that's more relevant, which causes damage to your reputation.

While you can use the search engine results page to see the publishing date, this may not always be accurate because the content could have been updated a few times.

Using the Internet Archive Wayback Machine can be helpful since you can find the original post.

For example, our "eCommerce SEO best practices" article was saved twice according to the Wayback Machine. 

Cite original sources

When you do research, the source is often mentioned in a popular article that ranks well on the search engines.

But in content marketing, it's a best practice to cite the original source when you refer to anything that isn't common knowledge or use information that needs to be cited.

Using the original study ensures that nothing is lost in translation (especially since the reader can go and check the data) and you are using the claims and facts in the right context.

How often have you checked a statement or fact in a blog article only to find that the source used and referred to doesn't support the statement?

Checking the source cited (and not simply using it without question) can help your content keep egg off its face. 

For medical content, using PubMed or NHIChelps ensure accurate first-hand information.

Separate facts and opinions

When dealing with AI generated content, go through the article to ensure that facts are stated as facts and backed up with verified sources, and that opinions or myths aren't passed as factual information.

When you aren't careful and don't make a clear distinction between facts and opinions, you are spreading misinformation, which hurts your credibility and relationship with your audience.

Opinions can easily be passed as fact, so follow up and question all statements before assuming they are facts. 

Even industry experts may see fact as fiction if insufficient data is available to verify the truth.

Using facts obtained by census or surveys can help determine whether something is true. 

For example, if you ask almost everyone about the memory span of a goldfish, they’ll tell you it’s roughly three seconds.

But if you fact check this information, you’ll learn that a goldfish can remember for weeks or months and not just the mythical three seconds people have learned by being misinformed.

Implement E-EAT suggestions

If your company is in the YMYL (Your Money or Your Life) space, it's essential to know that the content created in this niche is more strictly monitored by Google and their E-EAT (experience, expertise, authority, and trustworthiness) guidelines.

Because YMYL content potentially has a real-world impact on the reader's happiness, financial stability, health, or safety, you need to be extra careful and double check any facts, suggestions, and advice you share.

When AI generated content falls in the YMYL niche, ensure that subject matter experts fact check and edit the articles.

Another example of where AI may get it wrong is distinguishing between urban legend and actual medical or financial information.

While we know the “10-second rule” about food falling on the floor is absolute nonsense (and if you eat food off the floor, you can develop serious health issues), an AI won’t know this to be medically incorrect and not factual. 

However, if an SME is quoted as saying that showering daily is bad for you, it indicates that the information is factually based because of the SME’s credentials and experience.

Trusting expertise and facts in medical and financial fields is of the utmost importance, as misinformation can cost you millions in stocks or income and destroy your health.

Make fact checking AI generated content part of your publishing process

Every claim an AI writes is a draft claim until a person has traced it back to a source. Treat fact checking as a step in your publishing workflow rather than a one off cleanup: it sits between the draft and the publish button, and someone is named as responsible for it. Build the 7 steps into a checklist, highlight the facts before you start, and refuse to publish a number, quote, date, or study you have not opened yourself. AI writing tools can search the live web now, so gaps in training data explain fewer errors than they used to, but the tools still cannot confirm that a source says what they claim it says, and hallucination rates have not reached zero. The accountability for what goes live is yours, so don't assume the model knows more than your subject matter expert.