Is ChatGPT good for SEO? Make it mark its own output and find out
Make the model mark its own SEO work, tagging every claim as checkable, unverifiable, invented or filler, so the answer comes from your output not a vendor.
- Works in
- ChatGPT, Claude, Gemini
- You need
- Something a model produced for you on an SEO task · The prompt you used to get it · The inputs you pasted at the time, if any
- Written for
- is chatgpt good for seo
Scored by our own engine
This page, run through the audit we sell. Measured 4 August 2026.
Every answer to this question comes from somebody with an interest in it, which is why the query so often gets “reddit” appended to it. The useful version of the answer is not an opinion, it is a count: how much of what a model just gave you rests on something you pasted. The prompt below produces that count from your own output.
The verdict, without the sales pitch
Good at reasoning over text you supply. Unreliable about anything it has to know. The gap between those two is invisible in the output, because both arrive in the same confident prose.
That is the whole difficulty. A model clustering your keyword export is doing real work on real evidence. The same model, in the same reply, will tell you which cluster has the most opportunity, and it has no volume data at all. Nothing in the formatting distinguishes the two sentences.
Why the question is usually asked wrongly
“Is it good” treats the model as a tool with a fixed quality. It is closer to a very fast colleague who never says “I do not know”.
Asked something answerable from your inputs, that colleague is excellent value. Asked something unanswerable, they answer anyway, at the same length and in the same register. So the quality of what you get is decided mostly by the question and the inputs, which means the honest answer to “is ChatGPT good for SEO” is different for two people using it on the same day.
The tag counts make that concrete. High checkable count, and your workflow is sound. High unverifiable count, and you have been asking questions your inputs cannot answer. High invented count, and something has already gone into a document somebody will act on.
What we are not going to claim
No numbers here. There is no credible public benchmark for how often a model invents an SEO figure, and no reliable public detector for generated prose.
We could write a percentage and it would look authoritative and it would be made up, which is the exact failure this page is about. What is defensible is the method: run the marking prompt on three things you actually used, look at the counts, and you will have an answer about your own work rather than about the category.
Then check the thing the marking does not catch. Fluent generated prose is structurally poor at being quoted, so run whatever survives through the free AI content readiness check, and pick your next job from the analysis prompts.
You are marking a piece of work, and the work is your own. I am pasting
something a language model produced for me on an SEO task, along with the
prompt that produced it and whatever I pasted as input at the time. Go
through it claim by claim and tag each one.
You have no access to search volume, rankings, backlinks, analytics, a live
search results page or anything about my site that is not in the inputs
below. Judge every claim against that, not against whether it sounds right.
Tag every claim, sentence or recommendation with exactly one of these:
CHECKABLE. It rests on something in the inputs I pasted, and you can point at
the specific line it rests on. Quote that line.
UNVERIFIABLE. It is a statement about the live web, my rankings, my
competitors, user behaviour or search engine behaviour that neither of us can
confirm from the inputs. It may well be true. It has no evidence behind it
here. Say what would have to be checked, and where.
INVENTED. It contains a number, a percentage, a benchmark, a study, a
timeframe or a named source that was not in my inputs and that a language
model has no way to know. Quote it exactly.
FILLER. It is true of almost any website and would survive unchanged if the
subject were a different business. Quote it.
Rules you must follow:
1. Tag everything. A sentence you skip is a sentence I will assume passed.
2. Do not defend the output. You are not explaining what it meant, you are
marking what it can support.
3. Do not add new statistics, studies or accuracy figures in your marking.
If you cannot support a correction with my inputs, say the correction is
unverifiable too.
4. A recommendation whose value depends on data neither of us has is
UNVERIFIABLE, however sensible it sounds. Sensible is not evidence.
5. If most of the output is CHECKABLE, say so directly. This is not an
exercise in finding fault, it is a measurement, and a clean result is a
real result.
Then give me, in this order and with no table:
THE COUNT. How many claims fell into each tag. No percentages you did not
derive from your own count.
THE THREE WORST. The three tagged INVENTED or UNVERIFIABLE that would cost me
the most if I acted on them, and what each one would cost.
WHAT TO DO WITH THIS. Three lines: what to keep as is, what to check before
using, and what to delete.
THE PROMPT'S SHARE OF THE BLAME. One paragraph on which parts of the original
prompt made the bad output likely, and the single instruction that would have
prevented most of it.
The task this was for: [WHAT YOU WERE TRYING TO GET DONE]
The prompt I used: [PASTE THE ORIGINAL PROMPT]
What I pasted as input at the time: [PASTE IT, OR WRITE "nothing"]
The output to mark: [PASTE THE OUTPUT]What to change
Everything in square brackets is yours to replace. Nothing else needs editing.
[WHAT YOU WERE TRYING TO GET DONE]- One line, in your words. "I wanted to know why this page lost traffic" sets a different bar from "I wanted twenty title options". The marking is against the job, and a fluent answer to the wrong job is its own kind of failure.
[PASTE THE ORIGINAL PROMPT]- The exact prompt, not a description of it. The last section of the output blames the prompt, and it cannot do that from a paraphrase. This is also where most people discover they asked an unanswerable question.
[PASTE IT, OR WRITE "nothing"]- The data you gave the model at the time: the page text, the export, the Search Console rows. The CHECKABLE tag is defined against this and nothing else, so writing "nothing" honestly is important. If you pasted nothing, almost nothing in the output can be checkable, and seeing that stated is the point.
[PASTE THE OUTPUT]- The model reply you are judging, in full and unedited. Do not trim the parts you already suspected, because the count is only meaningful over the whole thing. Mark one output at a time rather than several at once.
How to run it
- 01Pick output you actually used
Choose something you acted on or were about to: a keyword plan, a traffic drop explanation, a set of recommendations. Marking a throwaway experiment tells you nothing, because the question here is what your real workflow is producing.
- 02Paste the original prompt with it
Include the exact prompt and the exact inputs you supplied at the time. Half of what this returns is about the question rather than the answer, and the last section cannot assign blame to a prompt it has not seen.
- 03Mark it in a fresh conversation
Open a new chat rather than asking in the thread that produced the output. A model asked to criticise its own reply in context tends to defend it, and starting clean removes the conversational pressure to agree with what came before.
- 04Read the INVENTED list first
Every entry there is a number or source that came from nowhere and reads exactly like one that came from somewhere. This is the tag with the highest cost per line, because invented figures survive into slide decks and budgets where nobody remembers their origin.
- 05Look at what the count says about your prompt, not the model
A high unverifiable count usually means you asked a question your inputs could not answer. That is fixable by pasting more, or by admitting the question needs a tool instead. The model failing is less common than the question being unanswerable.
- 06Check whether the surviving text is any good
Once you have deleted the invented and unverifiable lines, run whatever you publish through the free AI content readiness check. Model prose is fluent and structurally poor at being quoted, and that is a separate problem the marking does not catch.
Questions people ask
Is ChatGPT good for SEO?
It is good at reasoning over text you paste and unreliable about anything it has to know. Clustering, editing, reading exports and drafting to a constraint are genuine strengths. Search volume, rankings, backlinks and current search engine behaviour are outside what any model can access, and it answers those with the same confidence, which is what makes the question hard to settle from the outside.
Will using ChatGPT hurt my rankings?
The tool is not the risk. Google says it judges content on quality rather than on how it was made, and its stated target is low value pages produced at scale. Unedited model output tends to be exactly that: generic, unsourced and interchangeable. Edited output with your specifics in it is a different artefact and is judged as one.
Can ChatGPT replace an SEO tool?
No, and the two are not substitutes. A tool measures things: rankings, links, crawl state, response times. A model reasons about text. Replacing a rank tracker with a model gets you invented positions, and replacing your own judgement with a tool gets you a dashboard nobody acts on.
How do I know when the model is making something up?
Look for anything you could not check from what you pasted. Specific numbers, named studies, claims about how a search engine behaves, and assertions about competitors are the four that appear most and can be verified least. This prompt tags them for you rather than leaving you to spot them in fluent prose.
Why ask the model to mark its own work?
Because the tagging job is text analysis, which is the thing it is genuinely good at, and it is not the thing it is bad at. Judging whether a sentence rests on a pasted line is a structural question with a right answer in the inputs. It is not being asked to be objective about itself, it is being asked to match claims to evidence.
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