AI Blog Post Validator
Improve structure and clarity while keeping your argument, facts and publication style. Try the same tool available in your dashboard, then carry your input, options and result into your account.
Improve structure and clarity while keeping your argument, facts and publication style.
Add at least 40 words to draft article.
A stronger opening, useful section structure and an ending that fits the piece.
Five things to settle before a post goes live.
Ordered by what costs most when it is missed, which is not the order they are usually worried about in.
- Every fact, figure and date somebody could checkThe one that matters most and gets skipped most. Models produce confident specifics — statistics, dates, percentages, study findings — that are plausible and wrong, and a published post is permanent, indexable, and quotable by other people. Verify each figure against a source you can name, or remove it. A post with three fewer statistics and no invented ones is stronger.
- Links that resolve, and go where you saidTwo separate failures. Invented URLs that return 404s, and real URLs attached to a description of something else. The second is worse because it survives a link checker: the page loads, so an automated pass reports success, and the source does not say what your sentence claims it says.
- Quotations and attributionsMisattributed quotes are among the most confidently produced errors there are, because so many circulate already misattributed online. If a post quotes somebody, find the quote in a primary source before publishing. This is the error most likely to be corrected publicly by a reader.
- Whether it says anythingThe failure no tool detects. A post can be accurate, well structured, readable and add nothing — a survey of what is already written on the subject with no position, no evidence you gathered, nothing a reader could not get elsewhere. Ask what somebody knows after reading that they did not before. If the answer is thin, publishing it costs you more than not.
- Whether it exists already, in your own wordsTwo checks. Whether the content duplicates something already published, and whether it duplicates something on your own site — the more common problem, and the one that causes your own pages to compete with each other. If you have covered this before, updating the existing post usually beats adding a second one.
Detection is not the problem. Publishing something wrong is.
Most advice about AI-drafted content is about not getting caught, which gets the danger backwards. Nobody is running a detector on your blog. What happens to a published post is different and worse.
It is permanent and indexed. A draft with an invented statistic costs nothing. The same sentence published sits on your domain, gets crawled, and may be the thing an assistant cites when somebody asks about your subject. Errors in published content propagate rather than expire.
Fluency removes the friction that would make you check. This is the mechanism worth understanding. A draft that reads as finished does not prompt verification, and model output reads as finished by default. The posts most likely to contain an unchecked invented figure are the ones that came back needing the least editing.
Somebody will find it. Not immediately, and eventually — a reader who knows the subject, a competitor, somebody who follows the link. A public correction is a worse outcome than a slightly less impressive post would have been.
And the reputational cost is disproportionate. One fabricated statistic in a post makes every other figure on your site look uncertain, because a reader has no way to tell which ones you checked. That is the actual asymmetry: the upside of an unverified statistic is a slightly punchier paragraph, and the downside is your credibility on everything.
The practical rule that follows: if you cannot name the source, cut the claim. A post with three fewer statistics and none invented is stronger than the alternative, and it takes less time than verifying them would have.
The failure no tool detects.
A post can pass every check on this page — accurate, well sourced, readable, original, structurally sound — and still be worth nothing. This is the most common problem with AI-assisted content and there is no software answer to it.
The test is one question. What does somebody know after reading this that they did not before? Not what topic was covered. What specific thing did they learn. If the honest answer is that they now have a summary of what is already written elsewhere, publishing it adds a page to the internet and nothing else.
Models produce the average of what they read. That is what they are for, and it means an unassisted draft on a well-covered subject will reconstruct the consensus competently. The consensus is already published, usually by pages with more authority than yours.
What makes a post worth publishing is what only you have. Data you gathered. A case you worked on. A position you will defend. A mistake you made and what it cost. An assistant can help you write those up and cannot supply them, and the writing was never the hard part.
Search engines describe this as helpfulness. They have said the question is whether content is helpful rather than how it was produced, which is consistent with what actually happens: posts fail because they add nothing, not because a model was involved. The production method is a proxy for the cause.
Drafts from ChatGPT, Claude, Gemini, DeepSeek, Grok, LLaMA and Perplexity.
Each fails differently, and knowing which tells you what to check first rather than reading everything with equal suspicion.
ChatGPT blog post validator
The most fluent drafts and the most confident invented specifics.
ChatGPT produces posts that read as finished, which is precisely the problem: fluency removes the friction that would make you check. The statistics, study references and dates it supplies are plausible in form and frequently not real, and they sit in prose good enough that nothing prompts a second look.
Structurally it produces the recognisable shape — restated introduction, evenly weighted sections, a summarising conclusion — which readers now identify quickly. Breaking that matters more than any individual sentence.
Claude blog post validator
More cautious about facts, and it hedges the post into having no position.
Claude invents specifics less readily and is more likely to say it is uncertain, which makes it the safer starting point for anything factual. That is a genuine difference rather than a marginal one.
What it produces instead is balance: every view acknowledged, every claim qualified, no position taken. A post surveying four perspectives and committing to none reads as thorough and gives a reader nothing to take away, which is the emptiness check above.
Gemini blog post validator
Structure to check before content.
It returns headings and bullets by default, so drafts arrive fragmented into lists that were meant to be arguments. A post made of enumerated points reads as content rather than as writing, and the points frequently do not connect to each other because the list format removed the need.
The check worth running is whether each list is genuinely a list. If the three items are one idea split into three to look substantial, joining them back into a paragraph exposes that.
DeepSeek blog post validator
Check the top and tail before anything else.
Reasoning-model output frequently still carries the reasoning — an opening paragraph planning the post, or a preamble discussing how to approach the subject. Published, that is visible to every reader and to every crawler, and it stays up until somebody notices.
Its register also runs technical whatever the brief, which suits a documentation audience and reads as dense for a general blog readership.
Grok blog post validator
The best voice and the loosest relationship with verifiable claims.
Its drafts have genuine personality, which is the scarcest thing in published content and the hardest to add later. For a blog with a voice it is the most natural starting point of the assistants.
It also asserts most freely. Confident claims arrive without qualification, and confidence is not evidence. Every factual statement in a Grok draft needs checking before publication, and there tend to be more of them than in a comparable Claude draft.
LLaMA blog post validator
Read the whole draft, because quality varies inside it.
LLaMA is weights rather than a product, so output depends on the deployment. A post can be coherent for three sections and lose its thread in the fourth, which is unusual and easy to miss if you review by skimming.
Smaller deployments also produce factual errors at a higher rate, and errors in a published post are corrected in public if they are corrected at all.
Perplexity blog post validator
Real sources, and that makes one error much harder to catch.
Its citations generally point at documents that exist, which is a real advantage over purely generative drafts. The subtler failure is a real source attached to a claim it does not make — and because the link resolves, a link check passes and the error survives every automated review.
Open each source and find the passage supporting the sentence citing it. This is slower than a link check and it is the only thing that catches this particular error.
Blog post validation questions.
What does a blog post validator check?
The things that go wrong between a finished draft and a published post: facts and figures somebody could verify, links that resolve and say what you claimed, quotations and attributions, whether the post duplicates something you already published, and whether it actually tells a reader anything. It is a pre-publish checklist rather than another generator.
Will it tell me if my post reads as AI-written?
It flags the passages that read as generated, and if that is your main question the AI detector page covers the measurement properly. Worth saying that being detected is rarely the real risk in published content. Publishing something confidently wrong under your own domain is, because it stays there, gets indexed, and can be quoted back at you.
Can it check whether my facts are correct?
No, and no tool in this category can. It flags claims that look checkable so you know what to verify, and the verification is yours. Models produce plausible statistics, dates and study references that are simply invented, and a published post is permanent in a way a draft is not.
What is the most common problem with AI-drafted posts?
Emptiness, ahead of inaccuracy. A post can be accurate, well structured, readable and add nothing — a competent survey of what is already published, with no position and no information the reader could not get elsewhere. No tool detects this, and it is the reason most AI-assisted content underperforms.
Should I check links automatically?
It catches half the problem. An automated check finds URLs that 404 and cannot find a working URL attached to a claim the page does not support, which is the more damaging error and the one search-based assistants produce most. That one needs a person opening the source.
Does publishing AI content hurt SEO?
Search engines have said the question is whether content is helpful rather than how it was produced. In practice, the things that make AI-drafted posts unhelpful — no original information, no position, duplicating what already ranks — are what causes them to perform badly. The production method is a proxy for those; the causes are what matter.
Can it check posts drafted with ChatGPT, Claude or Gemini?
Yes, and the sections above cover what each leaves behind. In short: ChatGPT invents specifics most confidently, Claude hedges into having no position, Gemini fragments arguments into lists, DeepSeek sometimes leaves its reasoning at the top, and Perplexity produces the hardest error to catch — a real source cited for a claim it does not make.
How do I stop my own posts competing with each other?
Check whether you have covered the subject before, and if you have, update that post rather than publishing a second one. Two pages targeting the same intent split their signals and neither ranks as well as one would. This is the most common self-inflicted problem on sites publishing at volume.
How long should a blog post be?
As long as it takes to say the thing, which is usually shorter than the target somebody set. Length correlates with ranking because thorough answers tend to be longer, not because length causes ranking. Padding a post to reach a word count adds exactly the filler that makes content read as generated.
Is my draft stored?
The document is attached to your account so you can return to it, and you can delete it whenever you like. It is not used to train anything and it is never published anywhere.