Why Do GEO Visibility Scores Change When You Run the Same Prompt More than Once? A Tales from the Query Post

This article is part of a series for my blog called “Tales from the Query,” where I look at a random query in GSC and write a blog post about it.

Today’s query is: “why do geo visibility scores change when you run the same prompt more than once?”

I already have an article that ranks for this query, “What Does Your GEO Visibility Score Actually Measure?”:

But I believe the query is unique enough to deserve its own article.

So let’s get to it.

What’s the short answer?

GEO visibility scores change when you run the same prompt more than once because the score is measuring an observation from a probabilistic, multi-stage system, not a fixed ranking.

Let’s define those terms, probabilistic and multi-stage system.

Probabilistic means the AI is estimating what should come next based on probabilities, including choices like which brands to mention, which facts to emphasize, which sources to use, and how to order recommendations.

Multi-stage system means the final answer is usually produced through several separate steps rather than one single model decision.

Let’s know dive into individual reasons why GEO visibility scores change as the same prompt gets run multiple times:

1. Generation Varies

An LLM predicts tokens probabilistically, so identical prompts can produce different wording, entities, recommendations, and ordering.

2. The Decision to Search Can Vary

In search-enabled AI assistants, generating the answer is only part of the system. The assistant can first decide whether to search, which queries to issue, whether to refine them, and how many searches to perform. (A 2026 study of ChatGPT, Claude, Grok, and DeepSeek found substantial differences in search invocation and querying strategies.)

3. The Visible Prompt Isn’t Necessarily the Retrieval Query

AI assistants may rewrite one user prompt into one or more targeted searches (query fanout) and then issue additional searches (or take other retrieval actions) after inspecting the initial results.

4. Retrieval is Followed by Selection and Reranking

A source can be retrievable without entering the limited context used to construct an answer. Different retrieved evidence can therefore create different candidate brands, documents, and facts before generation even begins.

5. Being Retrieved, Influencing the Answer, Being Mentioned, and Being Cited Are Different Events

A study I looked at for Hamsterdam Research found claims supported by retrieved pages that weren’t visibly cited.

6. Brand Recommendation Itself Behaves Like Stochastic Retrieval and Ranking

Repeated responses to the same query produce different brands and rankings, according to studies, which is why some researchers propose measuring recommendation probability across repeated samples rather than inspecting one generated list.

7. GEO Tools Turn Variable Observations Into a Score

GEO visibility score precision can depend on the number of runs you do, according to research. If your brand appears in one run but disappears in the next, the mention/citation inputs to the visibility calculation have changed. If only a small number of observations are collected, those individual flips can noticeably move the reported number.

8. The Scoring Methodology Used Can Introduce Variation

Research shows that prompt selection, weighting, denominator rules, and even the instructions given to an LLM judge can change the reported score. This means there is both system variance in what the AI produces and measurement variance in how the tracking system turns outputs into a number.

9. Context Can Quietly Change the Effective Input

Location, conversation history, memory/personalization, UI versus API, model version, search settings, and date can all alter what looks like “the same prompt.”

10. The Underlying Systems Change Over Time

Models, indexes, retrieval systems, ranking algorithms, and web content aren’t frozen. This means that a Monday-vs-Friday difference isn’t necessarily stochasticity alone; it can also reflect genuine system or corpus drift.

Other sources worth mentioning:

A SparkToro/Gumshoe experiment had 600 volunteers run 12 prompts through ChatGPT, Claude, and Google’s AI systems 2,961 times. They found that for ChatGPT and Google’s AI systems, the same brand list appeared in fewer than 1 in 100 repeated responses, and getting the same list in the same order was closer to 1 in 1,000. That said, certain brands appeared more frequently than others. So we’re talking about unstable individual outputs but measurable appearance probabilities, the heart of GEO visibility measurement.

Another paper about the Dice Roll Method formalizes this takeaway statistically. It treats repeated identical prompts as samples from an underlying distribution and decomposes variable into sampling, prompt, run-t-run, and model-version effects. What’s important is that the paper doesn’t conclude that everyone should blindly run every prompt exactly a certain number of times, but rather reliability depends on the particular measurement setup.

So what’s the final answer to “why do GEO visibility scores change when you run the same prompt more than once?”

We could say that visibility scores change because LLMs use random sampling, but that would be incomplete.

Instead, we must distinguish between answer variance, retrieval variance, and measurement variance.

Answer variance is when the AI itself gives you a different result. Retrieval variance means a search-enabled AI can search differently, encounter a different candidate set, or select different sources. While measurement variance means the visibility tool samples outputs and applies its own prompt corpus, frequency, weighting, entity detection, citation rules, and aggregation formula.

That gives us a nice final takeaway: A changing visibility score doesn’t automatically mean your GEO performance actually improved or declined. Some portion of the movement may be sampling noise. The smaller the sample, the harder it is to separate a real change in visibility probability from ordinary run-to-run variance.

That gives us a nice final recommendation: Increase both the number of prompts you measure and the number of repeated runs, rather than relying heavily on either one alone. A larger sample (multiple runs of the same prompt) helps smooth out ordinary run-to-run variance. Meanwhile, a broader set of relevant prompts tells you whether you’re visible across the market or topic you’re trying to measure.

Outro

Thanks for checking out this Tales from the Query post answering “why do geo visibility scores change when you run the same prompt more than once?”

Stay tuned for more Tales from the Query posts, or check out past ones below.

Lastly, if you need a hand with your SEO/GEO strategies, feel free to contact me. I’m an independent consultant helping brands and agencies.

Until next time, enjoy the vibes:

Thanks for reading. Happy optimizing!

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