A marketer in r/SEO_LLM tried a week of "best tools for" prompts in ChatGPT and Gemini and noticed the same four to six brands in almost every answer. The top reply pushed back on what that meant.
Tested how often my brand appears in LLM answers… results were unexpected
I ran a small experiment this week. Tried multiple prompts across different LLMs like ChatGPT and Google Gemini: “Best tools for [niche]” “Top platforms for [use case]” “Recommended solutions for [problem]” And tracked which brands showed u...
Their point was that models hand back "probabilistic text, not ranked lists," so one answer isn't a ranking. That's true, and it's easier to act on once you know how a large language model works. So here's the plain-English version, with two small tests we ran on October 19, 2026, and what each part means for your content.
How a large language model works, in four parts
A large language model, or LLM, is a program trained on a huge amount of text to predict what comes next. Ask it a question and it writes the answer one small piece at a time, each piece chosen from what's likely to follow everything before it.
Four parts of that process matter for anyone publishing content.
- Tokens are how the model splits text into pieces it can work with.
- Training is where the model learns patterns, facts and which names go with which topics.
- Retrieval is how assistants like ChatGPT look up fresh pages before answering.
- Sampling is how the model picks each next piece, which is why answers vary.
Tokens: how a model reads your brand name
Models don't read words. They read tokens, which are common chunks of text. We ran 15 brand names through o200k_base, the open-source tokenizer OpenAI released with GPT-4o. All 15 split into two or three tokens.
| Word | Tokens | How it splits |
|---|---|---|
| plumber | 1 | plumber |
| Kubernetes | 1 | Kubernetes |
| ClickUp | 2 | Click + Up |
| Lemonade | 2 | Lemon + ade |
| Mailchimp | 2 | Mail + chimp |
| Pipedrive | 3 | P + iped + rive |
| Ruggable | 3 | R + ugg + able |
| Policygenius | 3 | Policy + gen + ius |
Common words get one token. A widely written-about name like Kubernetes gets its own token too. The brand names, though, were all built from pieces, and some of those pieces carry other meanings, like "Lemon" in Lemonade, an insurance company.
That's why context matters so much for a brand. The model links "Lemonade" to insurance only from the text around it. We'd call this the name-plus-noun habit. Write "Lemonade, the renters insurance app" often enough, on your site and elsewhere, and the pieces get tied to the right meaning. Search engines match whole phrases by meaning too, as our semantic search test shows.
For scale, this blog's 1,748-word post on brand sentiment came out at 2,411 tokens.
Names that look alike can blur. A business owner posted in r/ChatGPTcomplaints that someone looking for a different company with a similar name got a mix of that company's details with the poster's own website and phone number. The model joined two businesses whose names shared most of their pieces. Clear, repeated pairings of your name, city and category are the best guard against that.
Training: what the model already remembers
Training is where a model reads a vast amount of text and adjusts itself to predict it better. Facts, brand names and which products get mentioned together all come from that text. Then training stops, and the model's memory freezes at that point.
That frozen memory explains a common complaint. A growth marketer in r/growthmarketing asked ChatGPT what their company did and got a confident paragraph about a product they'd killed in 2022. The model was repeating what it had learned, not what's true now. The reverse holds too. A page you published last week can't be in any model's memory yet, so the only way it reaches an answer is through a live search.
So the pages that shape a model's memory are the ones that existed, and were widely copied, before its training ended. Our guide to getting into LLM training data covers how that text gets collected, and how long AI search takes to learn a brand covers the slow clock.
How often does ChatGPT mention your brand?
Most founders have no idea. The answer might surprise you.
Retrieval: how assistants read your page today
Memory alone goes stale, so assistants like ChatGPT, Perplexity and Gemini also search the web. They turn your question into one or more searches, pull back pages, read the relevant passages and write an answer that cites some of them.
The key word is passages. One poster in r/seogrowth looked at Gemini's network traffic and found it grounding answers on short extracts from page bodies, usually around 50 to 250 characters.
Chunking in AI SEO
I know “chunking” is a controversial topic in AI SEO. But I think people are mixing up two different things. LLMs can understand natural language. The problem is usually not the model reading your content. The problem is what gets crawled,...
A reply in that thread made a fair point. Writing a clear, short answer near the question has helped since the days of featured snippets. What changed is that an AI answer can lift that passage from anywhere on your page and quote it without the rest.
In the 15 answers from our test below, ChatGPT cited a median of nine sources and ran a single search in 13 of them. Our walk-through of how ChatGPT searches the web covers each step of that process.
Sampling: why the same question gets different answers
When a model picks the next token, it doesn't always take the single most likely one. It samples from the likely options, which keeps answers from sounding robotic but means the same question can come back different.
We asked ChatGPT five buyer questions three times each and compared the products it named.
| Question | Products named across 3 runs | In all 3 runs | Top pick |
|---|---|---|---|
| best crm for small business | 6 | 4 | HubSpot CRM each time |
| best accounting software for freelancers | 5 | 5 | Wave, then QuickBooks, then Wave |
| best mattress for side sleepers | 7 | 3 | Casper, then Helix twice |
| best project management tool for small teams | 5 | 5 | ClickUp each time |
| best email marketing platform for ecommerce | 5 | 5 | Klaviyo each time |
Across the 15 answers, 22 of the 28 products named showed up in all three runs. The changes came at the edges. Salesforce's starter plan dropped out of the CRM list after one run and Less Annoying CRM took its place, and the mattress list swapped brands most often. That category has dozens of similar products, so the model has more near-equal options to choose from. If you sell in a crowded category, expect more churn, and judge your visibility by how often you appear across many runs.
We'd call this the stable core and the rotating tail. A handful of products the model strongly links to a question show up every time, and the last spot or two rotates.
What gets a brand into the core? A marketing manager in r/OpenAI spent a month testing ChatGPT in their category. Their competitors showed up consistently despite weaker traditional SEO, and what they had in common was a heavier forum presence and more mentions on third-party blogs. That fits how training and retrieval both work, since a name that appears across many pages about a topic becomes the likely next word.
15 hours a week manually. Or 15 minutes with RankControl.
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What this means for your content
Each part of how LLMs work points to something you can do.
- Put answers in short, self-contained passages. Retrieval pulls passages, so the sentence that answers a question should make sense on its own.
- Use your name with what you do. Tokens mean nothing alone, so pair your brand with your category on your site, in listings and in profiles.
- Keep facts current everywhere. Training freezes old facts and retrieval reads whatever pages exist, so fix outdated prices and products on your site and on the sites that describe you.
- Get mentioned where your category gets discussed. The stable core is built from names that appear together across many pages, like comparison lists and forum threads.
- Measure across many runs. One answer is a sample, so track a set of questions over weeks and watch the trend.
Our brand sentiment study shows the third point in action. ChatGPT quoted prices, review scores and complaint counts straight from the pages it read.
The time this takes by hand
Here's a rough monthly budget for watching and improving how LLMs describe you, by our estimate.
| Task | Hours a month |
|---|---|
| Running 20 questions on four engines, several times | 3 to 5 |
| Logging names, sources and changes | 2 to 3 |
| Fixing facts on your site and listings | 2 to 4 |
| Writing pages with clear, quotable answers | 8 to 12 |
| Total | 15 to 24 |
Doing it by hand or with help
You can run all of this yourself with a spreadsheet and patience. Or RankControl can check 50 of your buyer questions every week on ChatGPT, Perplexity, Claude, Gemini, Grok and Google AI Mode, and its AI visibility tracking shows the trend instead of one answer. It also writes and publishes the articles that give those engines clear passages to quote, once you approve them.
AI search traffic grew 835% this year. Is your content ready?
RankControl generates 26 content formats optimized for ChatGPT, Claude, and Perplexity. Published on your domain, matched to your brand.
