What are the best part of using AI systems? I say speed because repetetive tasks get done fast. What about worst part? Accuracy. AI-generated content, created within seconds, can look polished and still contain wrong data, outdated references, or misleaded context.
Even when content is produced with AI, making it publish-ready takes time, because quality still requires review. Yet speed often sits at the top of the priority list. When time is tight, fact-checking is usually the first step to be dropped. A writer’s job is to write. An editor’s job is to improve clarity and structure. There is rarely time or budget set aside for fact-checking. That responsibility often falls on writers or editors. But having the same person handle the same draft at every stage isn’t ideal. Still, that is the reality for many teams. That reality shapes how I work and how I design my workflow.
As a writer, I want to deliver reliable and accurate content to my clients. To do that, I learned how to combine fact-checking with content writing. So Fact it Up! was born from this way of working. It was designed to save time during the verification stage, and can be used by writers, fact-checkers, or anyone responsible for accuacy.
This article shows how I use Fact it Up! to verify AI-generated content before it goes live. My focus is how a structured fact-checking step prevents avoidable errors and keeps published content reliable.
Highlights:
- AI creates content quickly but often fails to provide accurate or up-to-date data.
- Tight deadlines usually force teams to skip fact-checking, which creates a high risk for errors.
- Fact it Up! simplifies the process by identifying specific claims that need verification.
- High-priority items like statistics and reports are checked first to protect your credibility.
- The tool helps replace vague phrases with specific references to primary sources and academic journals.
- Human judgment remains the final step to ensure the context and tone are exactly right.
Table of Contents
A real editorial scenario
Let’s say we’re writing a blog post about “Brand Consistency and Consumer Trust in Digital Marketing” for a SaaS audience. The goal is to support brand authority and rely on data to strengthen credibility. The first draft is generated with AI. It’s readable, well structured, and properly formatted. At first glance, it looks ready to publish.
On paper, everything seems fine. In practice, time is the main constraint. The draft arrives close to the deadline, leaving little room for deep manual review. Verifying every sentence would slow the process. Skipping verification would save time but increase risk.
That pressure leads to a familiar editorial dilemma. Either check each claim one by one or trust the AI output and move forward. Both options have a cost. One costs time. The other costs reliability.
This is our example AI-generated text from the blog post:
“Many studies show that brand consistency directly increases consumer trust and repeat purchases. According to a 2023 global marketing report, 67% of consumers continue buying from brands they trust, while inconsistent messaging leads to higher churn rates. Research also suggests that stable brand communication improves long-term customer loyalty across digital channels.”
The Pragraph’s anotomy
The language was confident, the structure was clean, and the claims were framed as widely accepted. Nothing about it felt unusual or obviously incorrect during a quick read.
It included a clear time reference, a specific percentage, and a report that sounded authoritative. “According to a 2023 global marketing report” suggested recent research. “67% of consumers” added precision. Phrases like “many studies show” implied consensus without naming sources. Each element reinforced the others.
The paragraph feel safe because it matches patterns readers see every day. So what is the problem? It’s the combination of vague authority and specific numbers. That mix creates confidence without proof, which is why we need a fact check.
Identified claims in the paragraph:
- A statistical claim: 67% of consumers continue buying from brands they trust. This requires a verifiable data source. Exact numbers must be checked.
- A report reference: “A 2023 global marketing report.” This implies a real, recent publication. The report must exist and support the claim.
- General behavioral statements: “Many studies show” and references to long-term loyalty. These describe patterns but do not point to a specific source.
Not every claim deserves the same level of checking. Exact figures and named reports are high priority because they present themselves as facts. Broad statements about behavior are lower priority for verification but still need editorial control. In many cases, they need reframing to avoid presenting opinion as proven outcome.
Running it through Fact it Up!
Here, the goal is to test accuracy and clarity before proofreading. You can think of this as a review layer, similar to how an editor scans a draft for weak claims.
To begin, paste your text directly into Fact it Up! or click the Fact-Check button.
Step1: I copied and pasted my text after clicking on Fact-chek button. This is a part of the result:
Based on the tool’s output, the first claim was marked as accurate, with five sources suggested for verification. I checked each link to confirm they were active. All sources were accessible.
Even so, the result triggered an editorial note. The phrase “many studies” stood out as vague. The claim was supported, but the wording lacked precision. I took my notes for editorial improvement
Step2: To test the tool’s reliability, I reviewed the suggested sources one by one.
- The first primary source was a PDF titled Maintaining Narrative Consistency and Brand Identity, published in the International Journal of Society Reviews by Universitas Ottow Geissler (e-ISSN: 3030-802X). ⇒ As an academic journal article, it qualifies as a primary source rather than an opinion-based blog post.
- The paper was published in 2025, which makes it recent and relevant. I searched the document for the term “brand consistency” and located a section that directly supported the claim. The reference appeared in the conclusion, confirming alignment with the statement in the text.
- I then moved on to cross-verification. The second suggested primary source was another academic PDF, Brand Loyalty: Factors Influencing Repeat Purchases, published in the Journal of Emerging Technologies and Innovative Research (May 2018, Volume 5, Issue 5, ISSN: 2349-5162).
- Using the same approach, I quickly located the relevant passage supporting the claim.
Since the tool didn’t let me down, I continued verifying the suggested sources on my own. The third source was also presented as a primary source. This might not always be the same. Depending on the topic and the available research, the tool may surface fewer sources. That limitation is tied to the existing research landscape, not the workflow itself.
- The third source was an academic thesis titled Brand Trust & Image Impact on Consumer Behavior in Crisis, published through DiVA Portal. It was submitted to the School of Business, Society and Engineering as a Master’s thesis in Business Administration in October 2022.
- Based on the publication details, this qualifies as a primary source, and the date is recent enough to be considered current. I first searched the document for the phrase “brand consistency” but didn’t find a section that directly supported the claim. To maintain consistency in verification, I adjusted the approach and searched for “consumer behavior.” That led me to a relevant passage aligned with the intended meaning of the statement.
This screenshot covers only part of the topic. The full PDF explores the subject in more depth, and using different keywords makes it possible to reach relevant sections quickly during verification.
All three primary sources supported the same general claim. As the tool indicated, first statement was accurate but needed minor editorial adjustment.
A fact-checker’s role isn’t to make editorial edits. However, this tool is also designed for content creators who fact-check their own work, which is why these small editorial notes were included.
I also checked the remaining links suggested by the tool. They were active and correctly referenced, and I located the relevant sections using the same method. The publication dates were current.
When working with secondary sources, additional checks are still necessary because they can include interpretation or citation errors. In this case, three solid primary academic sources were enough, so I did not need to go deeper into secondary sources.
Step3: Now let’s move on to the second claim.
There were five sources again, but I couldn’t fit all of them into the screenshot. This time, the tool marked the claim as partially accurate, as shown in the screenshot.
Fact it Up! suggested that the “global marketing report” was actually the Edelman Trust Barometer and that it should be named directly. It also provided a revised sentence. Revisions are written to match the tone and style of the original text, with the goal of making them usable in your content with minimal adjustments.
The primary source for this claim was the 2022 Edelman Trust Barometer Special Report – The New Cascade of Influence. Since this claim is based on a number, the verification step is more specific. I searched the PDF for “67” to locate the relevant data point quickly.
Based on this data, the 67% figure is correct. However, the revised sentence still needed adjustment because the phrasing “they must trust a brand before they’ll continue buying its products or services” is stronger than what the data supports. The slide measures people who say they are more likely to stay loyal to and advocate for a brand when they fully trust it. It doesn’t claim that trust is a strict prerequisite for continued buying.
You can work with more accurate revisions, customized to your style and target audience.
- “Recent Edelman Trust Barometer findings indicate that 67% of people in 14 global markets say they are more likely to stay loyal to and advocate for a brand they fully trust, compared with one they don’t fully trust.”
- Or “According to the Edelman Trust Barometer, 67% of people in 14 global markets are more likely to remain loyal to brands they fully trust.”
Laslty, you can upload these data points into the fact-checking interface and continue the review by discussing them with the tool. When a conclusion is unclear, the tool can help examine the wording against the source more closely.
Why this isn’t just Googling manuel
Manual checking breaks down under time pressure. Searching each claim separately fragments attention and slows the review. Editors often stop once a source looks plausible, not once the claim is confirmed. This creates false confidence, especially when links exist but don’t support the exact wording.
At the same time, junior editors face a different risk. They may confirm that a source exists but miss whether the data actually matches the claim. Vague phrasing and softened cause-and-effect language often pass review because they sound reasonable. The issue isn’t effort, but experience and pattern recognition.
A similar gap appears in AI-only workflows. AI can generate fluent text and surface references, but it doesn’t assess claim strength. It tends to blend real sources with imprecise conclusions. Without a structured review step, these outputs move forward unchecked.
Fact it Up! adresses this problem by standardizing how claims are evaluated. It increases speed by narrowing attention to what matters. It improves consistency by applying the same verification logic to every draft. Over time, it also builds editorial memory, making similar risks easier to spot.
This claim-review workflow applies to blogs, landing pages, and AI-assisted content. The standard remains consistent even as formats and authors change.
Turning a tool into a system
Fact it Up! is one tool in my content creation process. It helps keep my workflow smoother and my content more reliable.
The tool clearly saves time. Manual source hunting is slow at today’s information volume, especially for small teams. Many can’t afford a fact-checker, or even a full-time editor. As a result, fact-checking is often pushed onto the writer, absorbed by the editor, or skipped altogether. When that happens, responsibility becomes blurred.
It was designed to reduce that overhead. It speeds up verification and creates room for editorial judgment without taking control away from the human reviewer. The goal isn’t to automate responsibility, but to make it manageable under real constraints.
That said, reliable-looking output isn’t the finish line. I still run additional checks myself, as shown earlier. The tool surfaces risks and weak points, while final accountability remains human. This is where it proves most useful: highlighting moments where wording outpaces the data or where meaning needs adjustment.
Fact it Up! is available only as part of the Fact-Checking Kit, and a discounted price is still available.
