The AI fact-checking process that prevents embarrassing errors
How to refine AI-generated content into usable drafts
Here’s a scenario that happens more often than anyone wants to admit: A company publishes and distributes a press release with AI-generated statistics. The numbers look credible. They support the argument perfectly. And they’re completely wrong.
Sometimes companies (or their competitors) catch these errors. But often mistakes like this just sit there, quietly undermining credibility with anyone who bothers to verify.
Publishing accurate, properly sourced/cited content is fundamental to building trust with your audience, and that’s why you can’t skip fact-checking AI content. Not if you want to avoid publishing errors that damage your reputation or damage your audience’s trust in you.
The problem with AI hallucination is that it doesn’t announce itself. Fake or wildly misinterpreted statistics look identical to real ones. Invented quotes read like actual ones. The tools present fiction and fact in the same authoritative voice, and it’s your job to suss out the difference.
In this post, we’ll cover a straightforward fact-checking process for your content. We’ll talk about what needs verification, how to verify it efficiently and what to do when you catch errors. We’ll also give you some tools that could make the process faster and smoother.
Why AI hallucinates (and why you can’t skip this step)
AI hallucination happens when tools generate information that sounds plausible but isn’t true. The system is predicting what might come next based on patterns in its training data. When it doesn’t know something, it fills the gap with what seems likely. It guesses confidently, like a coworker who hates saying “I don’t know.”
When that happens, you get output that reads “right” but contains errors you may not catch if you’re not checking.
Here’s what hallucinations look like in the wild:
Fabricated statistics: AI will generate specific percentages, dollar amounts or survey results that don’t exist. “64% of B2B buyers prefer...” sounds authoritative until you try to find the original source of the stat.
Invented quotes: The tool might attribute made-up statements to real people, or create entire interviews that didn’t happen.
Wrong dates: Product launches, company milestones, industry events — AI gets timelines wrong regularly, especially for recent developments.
Misattributed sources: AI might cite a real publication but mess up the details, reference studies that were never published or link claims to sources that don’t support them.
You can’t train AI out of this behavior. Hallucination is baked into how these systems work. Which means every AI-generated draft needs verification before it goes live.
Your fact-checking focus areas
When you’re fact-checking, work through your draft systematically, looking at each of these categories. You don’t need a journalism degree to do this well — just a method.
Technical details and domain expertise
For specialized claims about how something works, what regulations require or what industry standards dictate, consult reliable sources like official documentation, peer-reviewed research or regulatory websites. Better yet, get a subject matter expert to review your content before publication. Five minutes with someone who actually knows the topic catches errors you’d miss even with careful research.
Statistics
Any specific number needs verification. Search for the exact statistic plus key terms from the claim. Link to the original source for all statistics or numbers. If you find the original source, confirm that the context is accurate.
If you can’t find a credible source after 10 minutes of searching, delete the claim. A weaker argument you can verify and source correctly beats a strong statement you can’t.
Watch out for statistics that appear in lots of places online, but lack an original source. Many times, you’ll see the same stat quoted across dozens of blog posts and LinkedIn updates, but no one links back to actual research. These viral stats often trace back to a single unsourced claim that everyone repeated without checking.
Quotes
Search the exact quote in quotation marks along with the person’s name. Check whether the quote appears on reputable sites or in original sources like interviews, speeches or published articles.
Dates, timelines and historical claims
Verify product launches, company milestones and industry events with a quick search. Cross-reference a credible source for any date that matters to your argument. AI often gets recent events wrong or confuses similar announcements (e.g., tools might merge two product launches from the same company).
Superlatives
Claims like “first,” “only,” “largest” or “most popular” are easy to disprove if they’re wrong. Verify these before publication.
What to do when you find errors
When you catch a hallucination or error, you have two choices: Delete the claim entirely, or replace it with verified information.
Delete when you can’t quickly find accurate information to substitute. Your draft might lose some punch, but it keeps its credibility.
Replace when you can find the correct information within a few minutes of searching. If AI cited a wrong percentage but you locate the actual figure from a credible source, swap it in. If a quote is misattributed but you find the real source, update the attribution.
The tricky part is getting AI to fix errors without creating new ones. If you prompt with “Replace this stat with the correct one,” AI might just generate another hallucination. If you use AI to fix problems, ask for sources and double-check before you cite them.
Speed up verification with the right tools
A few tools can speed up your fact-checking process without adding complexity.
Google Scholar can help verify academic claims or track down original research. If an AI-draft cites a study, running a search on Scholar often indicates whether the work actually exists. But presence in Scholar alone isn’t enough. You still need to examine where the study was published and whether the findings truly support the claim — so dig in a bit!
Newsplunker (AskNews) helps you verify media coverage and news-driven claims. It aggregates stories from hundreds of global news sources (many languages) and reconstructs events into a structured news-knowledge graph. That makes it easier to see where a story originated, which outlets covered it, when it surfaced — and whether coverage is broad or limited. Especially useful when AI cites recent developments or public statements.
Your internal resources matter too. Maintain a living document of your own commonly cited sources, industry data, authoritative publications and statistics. Before launching a fresh search, check that list — it can save time and ensure consistency.
Use a repeatable verification checklist. Over time, you’ll spot recurring error patterns (like misstated release dates, misattributed claims, or bogus citations). Build those into your routine so fact-checking stays efficient and doesn’t lengthen every draft into an hours-long research project.
Fact-checking protects your credibility
AI drafts need verification every time. The statistics, quotes and claims that make your content convincing are also the elements most likely to be fabricated.
Think of the energy you spend fact-checking as a small investment compared to the damage of publishing false information.
Build the habit now. Create your checklist, bookmark your key sources and treat verification as a standard step between drafting and publishing.
If you want reliable, customer-focused content you don’t have to second-guess, Horizon Peak is here when you need us.
Check out the other articles in the series:
our AI writing process is broken — let’s rebuild it
How to create a simple brand guide that makes AI write like your company
A beginning guide to prompting like a writer
Structuring inputs and source material for AI drafting success
How to write content outlines that AI can actually follow
Iterating AI drafts: A practical guide for marketers
How to optimize your AI-drafted content for both search and AI citations