Learn why an AI model may produce varying responses to identical prompts submitted by different users.
Co-occurrence in LLMs: The Short Answer
Why do LLM users give different answers to identical questions? The answer often comes down to 'co-occurrence. When a user asks an LLM a question, the model retrieves a novel set of sources. Different sources link brands to different topics or keywords; therefore, the model builds a unique recommendation for every interaction.
This article explores how co-occurrence affects brands and how they can adjust their content and media strategies based on model behavior, retrieval mechanisms, training data, and user context.
Co-occurrence impacts consumer searches in large language models more than most technical optimizations. AI models don't rank like traditional search engines. LLMs construct each response one word at a time, with brand recommendations driven by two forms of co-occurrence. The first is historical: how often brands appeared alongside relevant terms in the model's training data. The second is contextual: how often those brands appear alongside key terms in the live search results the model reads when generating an answer.
What Co-occurrence Means in Plain Language
Co-occurrence counts how often brands appear alongside related topics like categories, competitor names, features, price discussions, and commonly used phrases.
LLMs learn through patterns rather than fact-checking. If a brand appears frequently alongside a query like 'best CRM for a five-person team' in its training data, the model will likely associate that brand with that search. Conversely, if a brand appears once or twice, the model may overlook it. Researchers call this effect 'co-occurrence bias.' This bias often causes models to favor statistically frequent word associations over accuracy, and that tendency persists even after scaling or fine-tuning the model (Kang & Choi, arXiv:2310.08256).
Layer One: Co-occurrence in LLM Training Data
Frequency has a measurable threshold. This study found that a stable internal link forms once a pair appears together 1,000 to 2,000 times in a models training data. Fall short of that count, and the model holds no dependable link between a brand and its category. From there, it reaches for whatever it links to, usually the market leader, and occasionally, a non-existent product.
Layer one explains a pattern that frustrates clients: a technically sound website with minimal third-party mentions often loses a recommendation to a weak competitor with mentions across multiple online sources.
Below roughly 1,000 co-occurrences, the model holds no dependable brand and category link and substitutes the market leader.
Layer Two: Co-occurrence Behavior in Live Search
Most brand-specific searches trigger a live web search, and that query decides the model's shortlist. One analysis of 46 business software prompts, tracked over four months, found that ChatGPT used web search 95.5% of the time and that 92.7% of the brands it recommended appeared on its cited pages. The same research found that a cited brand maintains a recommendation 75% of the time, while a brand missing from cited pages holds only 13% of the time.
A single query pulls roughly 20 to 40 URLs, and ChatGPT often relies on Bing's Index rather than Google's. Perplexity runs a separate pipeline, and Claude leans on Brave. These index dependencies mean that a page missing from one index cannot be read by the tool built on it.
Citation status, not site quality, decides whether a brand survives into the final answer.
Narrow searches help explain the split. A study of 22.7 million citations across five models found that 79.6% of cited sources appeared in only one model, and that Perplexity never cited 89.1% of the sources that ChatGPT cited for the same prompt. Brands aligned more often than web pages: models named the same brand 30.3% of the time while landing on the same page only 6.8%.
Brand-level associations carry across models. Individual URLs almost never do.
Seven Reasons Ten Identical Searches Create Ten Different Responses
Randomness inside the model.One lab sent an identical prompt 1,000 times with randomness switched off and received 80 different completions; the first 102 words matched, then the responses diverged. The cause results to the way servers batch requests, so its answer varies based on traffic from users. OpenAI states that its API runs non-deterministically by default and that its seed setting yields mostly repeatable output.
Wording. Reword one consumer question, and the overlap between the two brand lists drops to 0.288, versus a 0.50 to 0.61 baseline when running an identical prompt twice. Add a constraint, and overlap falls to 0.135.
Language.A study of 12,933 responses across 20 brands, 8 languages, and 3 models found that query language accounted for 26.5% of the variance, while brand identity accounted for 1.5%.
Question type.Comparison prompts recommended the same brands 30.1% of the time; background prompts about the same brands did so 7.5% of the time.
Timing. A Google AI summary carries about a 70% chance of varying between searches, and a given version lasts roughly 2.15 days.
Personal context.Memory, earlier turns in the chat, account settings, and rough location all factor at varying weights in the user's request.
Version drift. Vendors release model updates and retrieval changes without notice. Reddit's share of ChatGPT citations fell from roughly 60% of prompt responses in early August 2025 to about 10% by mid-September without a documented cause.
Seven layers of drift sit between the question and the answer, before anyone touches the content.
What Co-occurrence Means for AI Visibility Reporting
A single response doesn't provide much information. This variance study put the reliability of a single answer at about 0.01, and reliability climbed only to 0.36 once every language and model entered the design. Rerunning the same prompt a sixth time cuts the error by 0.0003, so the popular habit of averaging five runs provides minimal value. Adding languages, models, and paraphrases is a better use of resources. One informal study asked five questions over 100 times and received an identical set of recommendations in 23% of responses, and the same set in the same order in 7%.
Read the statistic for its denominator. Several viral claimsabout Reddit trace back to a single, misleading dataset: Reddit accounts for 1.8% of all ChatGPT citations, not the 11.3% that circulates, because the larger figure counts each model's top ten sources. Citation counts per answer disagree similarly, with one study putting ChatGPT at 7.92 sources and another at 13.3.
Reliability rises with breadth of design, not with repeated runs of the same prompt.
How Co-occurrence Moves a Brand Into LLM Recommendations
Third-party collaborations remain important. Ahrefs measured 75,000 brands and found off-site brand mentions tracked with AI summary visibility at 0.664, while backlinks managed 0.218.
Prioritize earned coverage. Controlled tests across several categories found AI search weighs heavily toward third-party sources and away from brand-owned pages and social posts.
Dominate pages with consistent citations. Comparison posts, roundups, review sites, and community threads are popular sources, and brands need to appear in them alongside relevant category terms.
Identify important use cases.Mention rates jump when content pairs a brand with a buying situation, which is what a comparison prompt asks for.
Treat page-level tweaks as supplemental support in an overall strategy. Structured data findings conflict across large studies, and one cross-platform study of 1,006 pages found schema no more common on cited pages than on uncited ones, 43.1% against 44.8%.
Be wary of popular "optimization playbooks". The original paper on optimizing for AI answers reported gains near 30% to 40% from adding quotes and statistics, but an independent replication found a real positive effect in only 3 of 54 cases, and adding statistics lowered rankings in 19 of 24 settings.
Ahrefs measured 75,000 brands: off-site mentions track AI visibility roughly three times harder than backlinks.
Co-occurrence in two sentences
"LLMs write novel answers for every prompt (question). Their answers depend on retrieved sources, and those sources differ by model, language, and minute. A strong search presence makes your brand a go-to source for AI models. Measure success using trends across models from a set of targeted, relevant prompts based on user needs (not "best " queries).
Co-occurrence and AI Search Challenges
Co-occurrence makes it hard to optimize for a single prompt, model, or page and expect consistent recommendations. Brands need repeated associations with the right categories, use cases, product attributes, and consumer needs across owned content, earned media, reviews, comparison pages, and other sources that models retrieve.
5WPR helps brands measure those patterns, identify the sources influencing model recommendations, and build the content and third-party authority needed to improve AI visibility. Explore 5WPR's AI Search and Generative Engine Optimization services to learn how your brand can earn more citations and recommendations across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
