ChatGPT SEO Prompts That Are Worth Saving

Search for ChatGPT SEO prompts and you get listicles with two hundred entries, most of which are one sentence long and produce output you would never ship. The useful set is much smaller, and it splits in a way those lists never mention: some prompts help you do SEO work, and some prompts are the thing you measure. If you run a small SaaS company, the second group is the one that tells you whether AI assistants are sending you buyers or quietly handing them to a competitor.

This is the shortlist I keep, why each one is shaped the way it is, and what to do with the output afterward.

What separates a prompt worth saving from filler

A one-line prompt like “give me keyword ideas for project management software” returns whatever the model considers average for the category. Average is exactly what you do not want, because your competitors get the same output from the same question.

The prompts that hold up have four things in them:

• A role and a stance. “Act as a skeptical reader who already tried two competitors” produces different output than “act as an SEO expert,” because the first one implies a job and the second one implies a tone.
• Your own data pasted in. The model has no idea what your churn looks like, which support tickets repeat, or what your pricing page actually says. Prompts that include this beat prompts that ask it to imagine.
• An explicit refusal condition. “If you do not have a basis for a number, say so instead of estimating” cuts the most common failure, which is confident invention.
• A fixed output format. A table with named columns is checkable. A wall of prose is not.

Everything below follows that shape. Adapt the bracketed parts, keep the constraints.

Prompts for keyword and intent work

The model is genuinely fast at generating phrasings and sorting them by intent. It is unreliable at anything numeric.

You are an SEO analyst. Seed term: "[term]".

List 25 phrasings a real buyer would type or say out loud when they have
the problem this term describes. For each one give:
- the phrasing
- intent: informational / comparison / transactional
- the decision the person is trying to make

Do not estimate search volume or difficulty. If a phrasing is one you
invented rather than one people plausibly use, mark it "speculative".
Output a markdown table.

The speculative flag is the part that earns its place. Without it the list looks uniformly plausible and you waste an afternoon checking phrases nobody types.

A second one, for finding the gap between what you publish and what buyers ask:

Here are the titles of every page on my site: [paste list].
Here are 25 buyer phrasings: [paste the table from the previous prompt].

Match each phrasing to the page that would answer it. Flag every
phrasing with no matching page, and every page that matches nothing.
Do not suggest new titles yet.

The “do not suggest yet” line matters. Ask for solutions in the same turn and the model rushes past the diagnosis to produce a content calendar you did not ask for.

Prompts for briefs, drafts, and editing

This is where most people use ChatGPT as a writer and get a page that reads like every other page in the category. Use it as a critic instead.

Here is my outline for an article targeting "[keyword]": [outline].

Act as a skeptical reader who has already tried two other tools in this
category and is not convinced they need a third. List every question you
would still have after reading this, ranked by how likely it is to stop
you from buying. For each, name the section that should answer it.

Do not rewrite the outline.

And the one I run on every draft before publishing:

Here is a draft: [paste].

Quote every sentence that a direct competitor could publish word for
word without changing anything. For each, say why it is interchangeable.
Do not suggest replacements.

That prompt is a proxy for information gain. Whatever it quotes is the part of your page that gives nobody, human or model, a reason to pick you. The fix is not better phrasing, it is adding specific citable facts and numbers that only you have: a benchmark you ran, a real pricing breakdown, a pattern from your own support inbox.

Prompts for technical and structured work

Schema markup is rigid and easy to validate, which makes it one of the few places where model output can go almost straight to production.

Generate Organization JSON-LD for [company].

Use only the facts below. Omit any property I have not given you a
value for, and do not invent sameAs URLs, founding dates, or logos.

Facts: [paste]

The omission instruction is doing real work. Left to itself the model fills in plausible-looking properties, and a sameAs pointing at a LinkedIn profile that does not exist is worse than no sameAs at all.

Three failure modes to expect

None of these prompts are reliable enough to run unattended. The failures are consistent enough to plan around, though.

It fabricates anything numeric. Search volumes, difficulty scores, market share, adoption percentages, the year a competitor was founded. It produces these in the same confident register as everything else, and there is no visual tell. Treat every number in the output as a claim to verify, not a fact to paste. The refusal condition in the prompt reduces this but does not remove it.

It flatters the input. Ask “is this outline good” and you will be told it is comprehensive and well structured. Ask what a reader would still not know and you get something usable. The difference is not politeness settings, it is that the second question has a checkable answer and the first does not. Write prompts that make agreement impossible.

It drifts toward the category average. Left unconstrained, output converges on the most common way the topic is discussed online, which is exactly the writing your competitors already published. This is why pasting your own data matters more than any clever phrasing. The model can only be specific about things you told it.

There is also a quieter failure worth naming: a long chat session accumulates context, and a prompt that worked on turn two behaves differently on turn thirty. For anything you plan to compare across weeks, start a fresh conversation each time.

The prompt category the listicles skip

Everything above helps you do the work faster. None of it tells you whether the work is landing in the place buyers now ask.

When someone types “best invoicing tool for freelancers” into ChatGPT, they get three to five names and a sentence each. That is the entire consideration set. If you are not in it, you were not rejected on features or price. You were never in the room. The mechanics behind this are worth understanding separately, and how ChatGPT actually recommends products covers the difference between ranking and being named.

The prompts you track look nothing like the ones above. They are short, they contain no instructions, and they never mention your brand:

What's the best [category] for [specific type of user]?

I need to [job to be done]. What tools should I look at?

[Competitor] vs the alternatives for [use case]. What else should I consider?

What do people use instead of [competitor] when [constraint]?

Four categories are enough to start: your core category, a job-to-be-done phrasing, a competitor-alternatives question, and one aimed at a specific segment you serve well. Keep the language plain. People asking an assistant do not write like they write search queries, so a prompt full of keyword-style phrasing tests something nobody actually asks. Read each one out loud, and if it sounds like a search box rather than a person, rewrite it. Add a brand-name question (“is [your product] good for [use case]”) only to check accuracy, not presence, because the answer is contaminated by the fact that you named yourself.

Here is the split that most prompt lists collapse into one:

	Work prompts	Tracked prompts
Purpose	Do a task faster	Measure whether you get named
Wording	Long, constrained, includes your data	Short, natural, no brand name
How often	Whenever you need them	Same wording, on a schedule
Output you keep	The draft or table	Whether your name appeared, and where
Changes allowed	Freely, per task	Never, or you lose the baseline

Turning a tracked prompt into a number

One run tells you almost nothing. Ask the same question three times and you can get your product in the answer twice and absent once, with no change on your side. That variance is inherent to how these models generate text, which is why a single check is a coin flip dressed up as data. The technique that fixes it is prompt sampling: run the identical prompt several times and record the share of runs where you appear, rather than a yes or no.

From there the workflow is mechanical:

1. Fix a prompt set and write the wording down verbatim. Twenty to thirty prompts covers a small SaaS category well, and how many prompts you actually need goes deeper on where the returns flatten.
2. Run each one three to five times, on a fixed schedule. Weekly is enough.
3. Record three things per run: whether your brand appeared, roughly where in the list, and which competitors appeared alongside you.
4. Watch the trend across weeks, not the individual answers.

Doing this by hand for thirty prompts across ChatGPT, Claude, Perplexity, and Gemini is a couple of hours a week of copy-paste, which is why it usually gets abandoned by week three. AskAiRank runs the same prompt set on a schedule and keeps the history, so the weekly question becomes “what moved” instead of “did I remember to check.” If you would rather see the manual version first, tracking your brand in ChatGPT walks through it step by step.

Start with four prompts, not two hundred

Pick one keyword you care about and run the intent table prompt on it. Run the interchangeability prompt on the page you already have for that keyword, and fix whatever it quotes. Then write down two buyer questions from your category, run each five times, and note how often your name comes up.

That takes under an hour and gives you one number worth watching. A saved list of two hundred prompts gives you none.

Frequently asked questions