At Gumshoe AI, we spend a lot of time with the models behind the curtain, studying how generative engines respond, evolve, and influence the way people make decisions. One of the most important (yet often overlooked) dimensions of that behavior is consistency: when you ask the same question multiple times, do you get the same answer?

And more importantly, can you trust what you’re seeing?

In this post, we share new research from our team that examines how consistently leading models, ChatGPT console and OpenAI Search API, respond to repeated prompts. What we found offers both reassurance and caution: the models are remarkably stable in some ways, but still carry enough variability that brands and marketers should tread thoughtfully.

Why This Matters

Generative engines are becoming the first stop for product discovery, comparisons, and decision-making. But AI isn’t a static index, it’s a probabilistic engine. That means responses can shift, sometimes subtly, even when nothing about your brand or content has changed.

If you’re building your strategy around AI visibility, it’s critical to understand:

• How stable AI-generated answers actually are
• What level of fluctuation is normal vs. a red flag
• How often your brand shows up, not just if it does

How We Measured Response Stability

We asked the same exact prompt to each model 10 times and compared their outputs pairwise, resulting in 45 comparisons per model. To quantify textual similarity, we used the ROUGE-1 F1 score, a standard Natural Language Processing (NLP) metric that captures how much overlap there is in word-level output.

Beyond surface text, we also analyzed:

• Which products were mentioned
• How consistently they appeared
• Whether their ranking or placement varied meaningfully

This gave us a deeper understanding of not just how models talk, but how they associate brands with a topic across multiple generations.

What We Found

The Good News: Semantic Stability Holds Strong

Across both models, response similarity scores were high: frequently exceeding 0.7 and often surpassing 0.9. That means the models are largely consistent in how they describe a topic and which information they surface.

Even with high ROUGE scores, we observed subtle differences in word choice, phrasing, or ordering—tiny shifts that didn’t alter the meaning, but could still influence how users perceive tone or intent. This aligns with the concept of semantic uncertainty: the idea that language models can express the same underlying meaning in different ways, introducing ambiguity in how responses are interpreted. As explored in Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation (Yuan et al., 2023), even semantically equivalent outputs can vary in form, which has real implications for brand consistency and message control.

For example:

“DuckDuckGo is known for robust data privacy controls” vs.

“DuckDuckGo places a strong emphasis on data privacy.”

Same intent, different language. And that matters when your brand is part of the answer.

Figure 1. ROUGE-1 F1 similarity matrices show that both ChatGPT and OpenAI’s Search API produce consistently similar responses across repeated prompts, with high overlap in phrasing indicating strong semantic stability in how AI models express information.

The More Important News: Product Mentions Remain Largely Stable

Despite surface-level text variations, key products consistently showed up across generations. Not only that, they tended to hold similar positions in the answer, such as Chemex appearing first or second in most outputs.

This suggests that while the model may change how it speaks, it’s fairly consistent in what it considers relevant.

Image 2. Comparison of ChatGPT and OpenAI Search API rankings reveals that key coffee products like Chemex and Hario V60 consistently appear with stable positioning across prompts, highlighting how brand visibility in AI-generated answers is both measurable and strategically actionable.

So… Can You Trust a Single Prompt Result?

Yes, but only if you understand the margin of variability.

A single AI answer gives you directional insight. But it’s not a verdict. The better approach is to test across multiple runs, look for the patterns, and track the outliers. That’s exactly what Gumshoe AI does.

Our platform monitors not just whether you appear in an AI-generated response, but:

• How frequently
• How prominently
• In what context
• And whether your competitors are displacing you over time

What This Means for Your Brand

For CMOs, product marketers, and brand managers, the implications are clear:

• LLM visibility is not a static metric, it is a longitudinal trend
• Your inclusion in AI answers is stable enough to be useful but variable enough to track
• Testing and monitoring are no longer optional, they’re essential to the success of your business

At Gumshoe AI, we’ve built a platform that helps brands understand and improve how they’re perceived by large language models (LLMs). We don’t just track performance in generative search, we also interpret it. Our system identifies which brands show up in AI answers, how consistently they’re mentioned, and what specific language LLMs use to describe them. Then we help you optimize your content to earn more citations, more often.

Because when buyers ask AI for product recommendations, the most important question becomes: Will your brand be part of the answer - not just once, but every time it matters?

Find out what AI thinks about your brand at gumshoe.ai

—
Nicholas Clark
Research at Gumshoe AI
PhD Candidate, Information Science | University of Washington