// HOW-TO · QUALITY

How to fact-check AI content before publishing (2026)

Fact-check AI content before you publish: a step-by-step verification process to catch hallucinated stats, dates, citations, and metadata in 2026.

Last verified · 2026-10-03 · by Moe Ameen

A generative model does not retrieve facts — it predicts a likely sequence of words from its training data, which is why a fluent, confident draft can still state a wrong date, an invented statistic, or a citation that does not exist. Google made the consequence explicit in a 2026 update to its generative-AI content guidance: it is "critical to manually factcheck and review all AI-generated content for accuracy and trustworthiness before publishing," and that review covers metadata — title elements, meta descriptions, structured data, and image alt text — because all of it can appear in Search results. The model cannot do this step for you; verification is a human job.

This is the mechanical version of that job: a repeatable pass you run on any AI draft before it ships. It is about accuracy only — not tone, not authorship (that's the [publish without a fake author](/how-to/publish-ai-content-without-a-fake-author) workflow), not detection. The goal is to leave nothing in the piece that a reader could be wrong about by trusting you. For the why behind the five error types and how to scale this, see the guide on [AI content fact-checking before publishing](/guides/ai-content-fact-checking-before-publishing).

The steps

Read the draft once and mark every checkable claim. Before verifying anything, do a single pass that highlights every statement a reader could act on being wrong — statistics and numbers, dates, names and quotes, prices and specs, and every cited source or link. Do not fix anything yet; just mark. This separates the small set of load-bearing claims from the prose around them, so your verification time goes to what matters instead of re-reading opinion.
Trace every statistic to a primary source. For each number the model produced, find the original source — the study, the dataset, the official report — open it, and confirm it actually says that figure. A stat attached to no source, or to a source that does not contain it, is the most common and most citable AI error. The rule is absolute: if you cannot trace a number to a primary source you can read, cut it, no matter how reasonable it sounds.
Confirm every date against the official source. Dates come out close-but-wrong because the model has no calendar — a launch off by a quarter, an effective date off by a year. Verify each one against the primary source for the thing being dated: the company's own announcement, the official page, the standard itself. Do not confirm a date from a secondary recap article, which often prints its own publish date by mistake and can be a day off the real event.
Click every link and audit every citation. Never assume a link works or says what the draft claims — open every one. A model will invent sources, cite a real author for something they never wrote, or produce a URL that 404s or lands somewhere unrelated. For a formal citation, cross-check the author, title, venue, date, and identifier together; a fabricated reference often gets one or two right and the rest wrong, which only surfaces when you check them against each other.
Verify specifics against the entity’s own current page. Prices, feature lists, technical specs, product names, and company details drift toward what is typical rather than what is true. Confirm any specific claim about a real product, person, or company against that entity's own current source — their pricing page, their docs, their site. This category is the one most likely to be out of date as well as wrong, so a source that was right last year is not automatically right now.
Fact-check the metadata, not just the body. Run the same check on the title, the meta description, the structured data, and the image alt text — Google named all four because they surface in Search results and are exactly the fields teams let a model fill in bulk and never re-read. A page can have a meticulously verified article and a hallucinated statistic sitting in its meta description, which is the line a searcher actually sees. If a model wrote it and it can appear publicly, it gets verified.
Use an AI fact-check tool to triage, not to sign off. A dedicated fact-checking tool can flag unsupported claims and cross-check stats, dates, and links faster than you can, which makes a useful first pass that tells you where to look hardest. It cannot catch a subtly-false synthesis of true facts, judge whether a real source supports the specific claim, or decide to cut a stat — and an LLM-based checker can hallucinate a verdict too. Let it narrow your work; keep the sign-off human.
Cut what you could not confirm, then gate the publish. Resolve every marked claim to verified, corrected, or removed — "probably true" is not a pass, because probably is how a wrong fact ends up ranked and quoted. Only once the body and the metadata are clean does the piece publish. Make that gate real: a draft should not reach a platform without a human action confirming the check happened, so verification is a step you cannot skip under deadline.

Common gotchas

Trusting fluency. A confident, well-written paragraph gives no signal about which of its facts are real — the polish is exactly what makes an AI error survive a casual read.
Asking the model to check itself. Self-verification produces more plausible text, not a fact-check; it inherits the same predict-words-not-retrieve-facts failure that created the error.
Confirming a date or stat from a recap article instead of the primary source. Secondary coverage often drifts a date by a day or restates a number wrong — go to the original.
Skipping the metadata. The title, meta description, schema, and alt text are generated in bulk and rarely re-read, so an unverified claim hides there while the body gets all the attention.
Treating an AI fact-check tool as approval. It triages; it cannot catch a plausible-but-false synthesis or make the editorial call to cut an unsourced stat.
Leaving a 'probably true' claim in. If you could not trace it to a source you trust, it comes out — an unverified line that ranks can be pulled into an AI answer and propagate well beyond your page.

Legal note

In regulated fields — legal, medical, financial, tax, or safety — raise the bar from "traceable to a reputable source" to "confirmed against the authoritative or official source," typically a government, standards-body, or primary institutional source. A hallucinated figure in these domains is not just an SEO liability; it is guidance someone may act on. When a model produces a claim you cannot verify officially, remove it, and route anything consequential to a qualified human who can state it correctly.

Where Kompozy fits

The hardest part of this checklist is not any single step — it is doing it every single time, under deadline, when a draft looks finished. [Kompozy](/) is a full AI content generation and multi-platform publishing engine, and the specific thing it does for this task is make the gate in step eight structural: nothing it generates reaches a platform without landing in a per-post review queue first, so the "verify before you publish" checkpoint is a wall the content has to pass through, not a discipline you have to remember to add at the end.

There is also a quieter advantage upstream of the checklist. A lot of what Kompozy produces is net-new branded content built from your [Persona Brief](/glossary/persona-brief) — [Quote Graphics, Carousels](/glossary/output-buckets), [Persona Tweets](/glossary/persona-tweet), and avatar scene video — that asserts your own point of view rather than importing a pile of external statistics and citations. A piece that carries your perspective has a smaller surface of third-party claims to trace than a research-heavy article, which means the claim sweep in step one is shorter per piece. When a format does pull in facts — a blog post or newsletter — the review surface shows the body and its metadata together, which is the concrete answer to step six: the title and alt text get looked at, not auto-filled and forgotten.

Be clear on the honest limit: Kompozy does not run steps two through five for you. It cannot trace your stat to a primary source or click your links, because the whole premise is that a model cannot verify its own output — that human judgment is deliberately kept in your hands. What it removes is the drafting cost and the per-platform duplication, so your scarce verification attention goes entirely to the part only a person can do, and [Autopilot](/glossary/autopilot) only ever schedules batches you have already cleared through the gate. Starter is $199/mo (5,500 credits) for a solo creator; Pro is $499/mo (18,000 credits) for daily, multi-platform output with autopilot; Enterprise is custom.

Frequently asked questions

How do I fact-check AI-generated content before publishing?

Mark every checkable claim in the draft — statistics, dates, names, quotes, prices, and every cited source or link — then verify each one against a primary source: the original study, the official page, the company's own announcement. Click every link rather than trusting it. Cut anything you cannot confirm, run the same check on the metadata (title, meta description, alt text, structured data), and only publish once the body and metadata are both clean. The model cannot verify itself, so this step is a human job.

Can I just ask the AI to fact-check its own output?

No. A generative model predicts likely word sequences rather than retrieving facts, so asking it to verify its own work produces more plausible-sounding text, not a reliable check — it inherits the exact failure that created the error. Even a model with live web access can mis-summarize a page or over-trust a weak source. Verification has to come from outside the model: a human reading claims against an authoritative source, or a separate tool whose output a person still judges.

Do I really need to fact-check the title and alt text?

Yes. Google's 2026 guidance explicitly extended the review to metadata — title elements, meta descriptions, structured data, and image alt text — because all of it can appear in Search results. These fields are generated in bulk and rarely re-read, so a page can have a verified body and a hallucinated stat in its meta description. If a model produced it and it can surface publicly, it gets the same check as the body copy.

Do AI fact-checking tools actually work?

They help as a first pass. A good tool flags unsupported claims and cross-checks statistics, dates, and links faster than a person, which narrows what you review. What it cannot do is catch a subtly-false synthesis of true facts, judge whether a real source supports the specific claim, or make the editorial decision to cut an unsourced stat — and an LLM-based checker can hallucinate a verdict too. Use it to triage, not to sign off.

What AI errors should I look for first?

Five recur. Fabricated statistics with no real source. Dates that are close but wrong. Hallucinated citations and dead or mismatched links. Confident-but-wrong specifics like a misstated price, spec, or name. And the most dangerous, a plausible synthesis that blends real facts into a subtly false conclusion, sometimes dressed with a real-looking source. The first four fall to a source check; the fifth needs a reviewer who understands the subject reading the argument itself.