Does prompt wording change your AI visibility score?

Yes, and by more than most visibility dashboards could survive. The same underlying need, written in industry jargon, returns several times more brand mentions than when a buyer describes their situation in their own words. So the wording of your prompt set decides a large part of your visibility score before a single answer is read.

The way a prompt reads is its buyer register. This piece shows how big the register effect is, why it happens, and how to check which register your set is written in.

How big is the gap between jargon and buyer situations?

About 6.6 times, in the cleanest published test. Go Fish Digital wrote the same needs at three registers and ran them through ChatGPT, Google AI Overviews and Perplexity between 20 and 26 July 2026. Across 73 prompt variations and 1,554 completed responses, with most prompt and platform pairs run seven times, the brand was named at these rates:

A set written mostly in category terms would report something close to the first figure. A buyer who describes their situation to an assistant sees something closer to the last.

Is it really wording, or just buying intent?

Mostly wording. Category language often signals someone further along, ready to buy, and that alone could explain more brand names in the answer. Go Fish tested that by narrowing to the 49 prompts tagged as commercial, which holds buying intent roughly steady. The gap remained: the brand was named in 24.0% of answers with the industry label and 8.6% without it.

So even among buyers who are clearly shopping, the words they use change whether you appear. Two prompts with the same intent can produce very different visibility figures depending on whether they name the category.

Does the gap differ between assistants?

Yes, sharply. The same study broke the two extreme registers out by platform:

On Perplexity the buyer-situation prompts never named the brand at all. A single blended figure across assistants and registers hides all of this, which is why the study's own recommendation is to report the distribution, not only the average: break results out by platform, prompt family and run frequency.

Why does phrasing change the answer?

Because the wording tells the assistant what kind of answer to give. A category label says the person knows what they want, so the natural answer is a list of providers, which is a list of brand names. A situation says the person needs the problem explained first, so the answer spends its length on causes and options, with fewer brands in it.

A separate study points the same way. Peec AI analysed 37,804 AI responses across ChatGPT, Gemini, Perplexity, Google AI Mode and Google AI Overviews. Keyword-style prompts showed up to 25% higher average visibility than conversational ones, and ranking prompts about 20% higher. Prompts in the middle of the buying journey were highly sensitive to small wording changes. The study's advice was to anchor prompts to real buyer language, avoid mixing styles, and report each assistant separately.

How can you tell which register your set is written in?

Measure it, prompt by prompt. The RateMyPrompts scorecard computes a register profile for every upload using fixed rules, with no model involved. A prompt is keyword-shaped if it has six words or fewer and no question mark. It is written in the first person if it uses words such as "we", "our" or "I". It describes a situation if it contains phrases such as "we're", "we need", "trying to" or "currently using". It carries a qualifier if it names a size, budget, audience, integration, geography or regulation.

Here is what those rules report for three small example sets, four prompts each, covering the same need at each register:

The scorecard flags the first set as reading like search keywords, and likely to report a flattering visibility number. It describes the other two as reading the way buyers talk. That is a real limit worth knowing: the rules separate keyword sets from natural ones well, but a problem-led set with some first-person wording clears the same bar as a full buyer situation. Tagging your own prompts by register is still worth doing by hand.

What does the gap tell you?

More than either number alone. The distance between your category-led and buyer-situation results shows whether you are visible only to people who already know the category label. A wide gap means buyers who describe their problem, rather than naming the category, rarely hear about you. A narrow gap means you are part of the answer however people ask.

Nobody sees that distance in a blended figure. It only appears when the set contains both registers and you report them separately. It is also a better thing to try to move than the headline rate, because closing it means reaching buyers who have not yet learned the vocabulary.

What to do

1. Tag every prompt with its register: category-led, problem-led or buyer situation.
2. Keep all three in the set. Category-led prompts are legitimate; a set made mostly of them is not a measure of what buyers see.
3. Report registers separately, and each assistant separately, before any blended figure.
4. Track the gap between category-led and buyer-situation results over time, not just the average.
5. Rewrite keyword-shaped prompts as situations, using the phrases your buyers actually use. How to build a prompt set from buyer language shows where to find them.
6. Keep your own brand name out of all three registers. Branded prompts inflate every figure; see why branded prompts inflate AI visibility.

How register sits alongside the other checks in the Prompt Fit Score is covered in the methodology. To see your own set's register profile, run it through the free RateMyPrompts scorecard.