How Perplexity, ChatGPT and Gemini Pick Their Sources
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Influencing Sources You Do Not Own The highest value work sits on pages your team cannot edit. Review platforms, directories, forum threads and comparison articles carry disproportionate weight in generated answers, and getting represented accurately on them requires outreach, correction requests and occasionally patience with people who are not obliged to help.
Run each prompt at least three times. Assistants vary their answers between runs, and a single result is a sample rather than a finding. Record the full text of each answer and every source cited, not a summary.
The reasonable reading is that ranking gets a page considered while quotability and corroboration decide whether it is used. Treating a strong search position as an entitlement to appear in answers is the mistake that catches out established brands most often.
Where Marketplaces Fit Marketplace listings are frequently cited, and they are a mixed blessing. They provide corroboration and structured data you did not have to build, and they put a description of your product in circulation that you only partly control.
The writing skill sits in the middle. It can be taught to a good writer in a few weeks, and having it in-house pays off permanently, because every page you publish afterwards is better for it. The main obstacle is not difficulty but reluctance, since writing to be quoted means surrendering some of the control that persuasive copy provides. llm visibility tracking
Gemini and Google Surfaces Closest to conventional search infrastructure, which has a practical consequence: work that improves your standing in Google search tends to carry over here more than it does elsewhere.
How to Test Rather Than Trust Everything above is a starting hypothesis. Run twenty prompts in your own category across all three, from signed out sessions, recording the mode and the date, and count the cited domains for each.
You will find your own category's pattern, which frequently contradicts the general one. Some industries are dominated by a single trade directory. Others are dominated by one forum. That specific finding is worth more than any general description of how these systems behave.
What We Genuinely Do Not Know Several things are worth admitting rather than papering over. We do not know how the systems weight their signals against each other. We do not know how much residual influence training data has once retrieval is involved. We cannot reliably distinguish a change in your visibility from a change in the model's behaviour.
What to Do First Run five prompts describing a purchase your best customer would be making, from a signed out session, and see what gets named and cited. Then check whether your product data survives with scripts disabled, and whether your name and identifiers are consistent across every listing you can find.
Direct Answers Beat Positioning When a model composes a recommendation it needs sentences it can attribute. Positioning language supplies none. A paragraph about being a trusted leader committed to excellence contains no attachable claim, so it is passed over in favour of a competitor who wrote down their turnaround time.
Write between fifty and two hundred prompts covering five types: the category question, the problem question, the comparison question, the question that names a competitor, and the question that names you directly. The last one matters because it reveals what an assistant believes about you specifically, which is often more alarming than being absent.
They also appear in the roundups by default, because journalists writing about a category name the companies everyone knows. You cannot short circuit that, and pretending otherwise wastes the effort that should be going into a narrower position where the incumbents are absent.
It is also worth checking which assistant your customers actually use rather than assuming. The answer varies by profession, age and country far more than industry commentary suggests, and several businesses have built measurement programmes around a system their buyers never open. Adding one question to your enquiry form settles it in a fortnight and can redirect the whole effort.
A retrieval fetch reads text present in the response. If your dimensions, materials, compatibility and price are not there as text, they do not exist for this purpose, however clearly they display in a browser.
Entity Coherence Before a model can recommend you it has to be confident that the scattered mentions of your name refer to one company. That confidence comes from consistency across the details that identify you.
Where the Small Brand Genuinely Loses Being honest about this matters, since a plan built on ignoring it will fail. Large brands have accumulated press coverage, review volume and a settled entity record that took years to build, and those carry real weight.
Writing Prompts That Sound Like Customers The foundational skill is deceptively mundane. Somebody has to write the questions your buyers actually ask, in their words, without the category vocabulary your team uses internally.
Run each prompt at least three times. Assistants vary their answers between runs, and a single result is a sample rather than a finding. Record the full text of each answer and every source cited, not a summary.
The reasonable reading is that ranking gets a page considered while quotability and corroboration decide whether it is used. Treating a strong search position as an entitlement to appear in answers is the mistake that catches out established brands most often.
Where Marketplaces Fit Marketplace listings are frequently cited, and they are a mixed blessing. They provide corroboration and structured data you did not have to build, and they put a description of your product in circulation that you only partly control.
The writing skill sits in the middle. It can be taught to a good writer in a few weeks, and having it in-house pays off permanently, because every page you publish afterwards is better for it. The main obstacle is not difficulty but reluctance, since writing to be quoted means surrendering some of the control that persuasive copy provides. llm visibility tracking
Gemini and Google Surfaces Closest to conventional search infrastructure, which has a practical consequence: work that improves your standing in Google search tends to carry over here more than it does elsewhere.
How to Test Rather Than Trust Everything above is a starting hypothesis. Run twenty prompts in your own category across all three, from signed out sessions, recording the mode and the date, and count the cited domains for each.
You will find your own category's pattern, which frequently contradicts the general one. Some industries are dominated by a single trade directory. Others are dominated by one forum. That specific finding is worth more than any general description of how these systems behave.
What We Genuinely Do Not Know Several things are worth admitting rather than papering over. We do not know how the systems weight their signals against each other. We do not know how much residual influence training data has once retrieval is involved. We cannot reliably distinguish a change in your visibility from a change in the model's behaviour.
What to Do First Run five prompts describing a purchase your best customer would be making, from a signed out session, and see what gets named and cited. Then check whether your product data survives with scripts disabled, and whether your name and identifiers are consistent across every listing you can find.
Direct Answers Beat Positioning When a model composes a recommendation it needs sentences it can attribute. Positioning language supplies none. A paragraph about being a trusted leader committed to excellence contains no attachable claim, so it is passed over in favour of a competitor who wrote down their turnaround time.
Write between fifty and two hundred prompts covering five types: the category question, the problem question, the comparison question, the question that names a competitor, and the question that names you directly. The last one matters because it reveals what an assistant believes about you specifically, which is often more alarming than being absent.
They also appear in the roundups by default, because journalists writing about a category name the companies everyone knows. You cannot short circuit that, and pretending otherwise wastes the effort that should be going into a narrower position where the incumbents are absent.
It is also worth checking which assistant your customers actually use rather than assuming. The answer varies by profession, age and country far more than industry commentary suggests, and several businesses have built measurement programmes around a system their buyers never open. Adding one question to your enquiry form settles it in a fortnight and can redirect the whole effort.
A retrieval fetch reads text present in the response. If your dimensions, materials, compatibility and price are not there as text, they do not exist for this purpose, however clearly they display in a browser.
Entity Coherence Before a model can recommend you it has to be confident that the scattered mentions of your name refer to one company. That confidence comes from consistency across the details that identify you.
Where the Small Brand Genuinely Loses Being honest about this matters, since a plan built on ignoring it will fail. Large brands have accumulated press coverage, review volume and a settled entity record that took years to build, and those carry real weight.
Writing Prompts That Sound Like Customers The foundational skill is deceptively mundane. Somebody has to write the questions your buyers actually ask, in their words, without the category vocabulary your team uses internally.
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