What Is LLM Tracking and How Does It Measure a Restaurant's AI Visibility?
By ReleVantage. Last reviewed September 10, 2026.
Short answer
LLM tracking is the practice of running a fixed set of diner questions through AI assistants on a repeating schedule and recording what each answer actually says about your restaurant. Instead of a single ranking number, it produces several separate signals: whether you are mentioned, where you appear inside the answer, how you are described, whether the description is accurate, which sources the answer cites, how varied those sources are, and whether reservations or inquiries follow. ReleVantage tracks ChatGPT, Gemini, and Google AI Mode.
What does LLM tracking actually record?
An AI assistant does not return a list of ten links. It composes an answer, sometimes with a handful of supporting sources. So the unit of measurement is the answer itself, not a position in a results page.
LLM tracking means asking the same questions repeatedly, keeping the full response text and the cited sources, and then scoring that response against defined criteria. The value comes from the repetition: one screenshot tells you almost nothing, while the same question asked on a schedule over weeks shows whether the description of your restaurant is stable, improving, or drifting.
- A fixed, documented question set rather than ad hoc prompts
- The full answer text stored, not just a yes or no on the restaurant name
- The cited sources stored alongside the answer
- The engine and the date of every run
- A consistent scoring method applied the same way each time
How is it different from rank tracking?
Rank tracking answers one question: for this keyword, what position does this URL hold? The output is ordinal, but rank results also vary by location, device, and time, so a single snapshot is not a fixed verdict.
LLM tracking answers a messier question: when a diner asks this in natural language, what does the assistant say? Answers vary between runs even with identical wording, they can cite pages that do not rank well, and they can omit pages that do. A page can hold the top organic position and never appear in the generated answer, and a third-party article can be cited when your own site is not.
Variability is part of the data
Because generated answers are probabilistic, a single run is a sample and not a verdict. The correct response is to run each question multiple times per period and report how often a result occurs, rather than to treat one run as fact.
The competitive set is different too
In an answer, the competitors are whoever the model names in the same paragraph as you. That is often not the same list as the companies ranking beside you on a results page, and it can differ by engine and by question.
Which signals should be tracked separately?
Collapsing everything into a single visibility score hides the part you can act on. Keep the following distinct, per engine and per question.
Visibility, or mention rate
The share of runs in which the restaurant is named at all. Mention rate is one signal; citations and accuracy can still matter when a restaurant is not named.
Position within the answer
Being named first in a shortlist is not the same as being named in a closing aside. Record where in the response the restaurant appears and whether it is presented as a primary recommendation or an also-ran.
Citations and their targets
A mention is the model saying your name. A citation is the model attributing part of the answer to a source, usually with a link. Track both, and track whether the cited source is your own domain or a third party discussing you, because the work required to earn each is different.
Accuracy
Every factual claim the answer makes about you: services, coverage, locations, pricing posture, ownership, specialisms. Wrong facts can come from outdated sources, inference errors, or generation errors; they do not usually trace to a single source, so accuracy review is where the highest-value fixes tend to surface.
Sentiment and framing
How the restaurant is characterised: recommended, hedged, caveated, or described in terms that undersell what you do. Sentiment here is a human reading of the answer text, not an automatic score to be trusted blindly.
Source diversity
How many distinct domains the answers draw on, and how dependent your visibility is on a single source. Dependence on one source can increase volatility; broader independent corroboration may reduce that dependence, without guaranteeing stability.
Business outcomes
Referral sessions from assistant surfaces where analytics expose them, branded search demand, and the enquiries a business itself attributes to an AI conversation. These are supporting evidence, not proof of causation.
What does a repeatable measurement process look like?
- Define the question set from real diner language, segmented by intent, service, geography or use case, and category comparisons
- Freeze the wording; a changed question starts a new series
- Record a baseline before any optimization work begins
- Run every question on every tracked engine on a fixed schedule, several times per period
- Store the full answer and its sources, not a summary
- Score visibility, position, citations, accuracy, sentiment, and source diversity with the same rubric each time
- Compare complete periods against the baseline rather than day to day
- Log every change you make to the site, profiles, and third-party sources so movement can be interpreted against actual work
What are the limits of this data?
Answers vary between runs, personalisation and location can change what a user sees, and each platform refreshes on its own schedule, so a change to your site may not show up for weeks.
Attribution is also genuinely limited. Assistant traffic is often under-reported or arrives with no usable referrer, and a diner may read an AI answer and then arrive through a branded search days later. Treat outcome data as directional support alongside the answer-level measurements, and be suspicious of anyone who presents it as clean causation.
For the metric definitions and reporting cadence in more depth, see the measurement guide; for evaluating tools and providers against these criteria, see the diner's checklist.
How do you turn tracking into action?
Tracking on its own changes nothing. The point of separating the signals is that each one implies a different response.
- Not mentioned at all: the entity and the evidence behind the question are missing, so the work is content and corroboration
- Mentioned but never cited: the model knows of you but is quoting someone else, so the work is clear answers on your own pages plus better coverage in the sources it quotes
- Cited but inaccurate: find the source carrying the wrong fact and correct it at origin, then make the correct version easy to verify
- Cited through one domain only: broaden source diversity so visibility is not hostage to a single page
- Named but framed weakly: the description a model can assemble is thin, so the work is clearer positioning and specific, verifiable detail
Frequently asked questions
What is LLM tracking?
Running a fixed set of diner questions through AI assistants on a schedule and recording what each answer says about your restaurant, including the full response text and the sources it cites.
Is LLM tracking the same as rank tracking?
No. Rank tracking records a position for a keyword. LLM tracking records what a generated answer says, which sources it credits, and how accurate and favourable the description is. A page can rank first and never be quoted.
Which engines does ReleVantage track?
Exactly three: ChatGPT, Gemini, and Google AI Mode. We do not claim active tracking of any other engine.
How often should questions be run?
On a fixed schedule with several runs per question per period. ReleVantage scans tracked questions daily so variability is visible rather than hidden by a single sample.
Why do answers change between runs?
Generated answers are probabilistic and retrieval changes over time. That is why frequency and repeat runs matter more than any single result.
Can AI visibility be tied directly to revenue?
Not cleanly. Referral data from assistants is incomplete, and diners often arrive later through another channel. Outcome data is supporting evidence, not proof of causation.
How many questions should be tracked?
Enough to cover the questions that actually precede a purchase in your category. ReleVantage tracks 25 questions on Local and 100 on Pro, with additional questions available.
Keep reading
Want this applied to your brand?
We start with a recorded baseline of how ChatGPT, Gemini, and Google AI Mode describe your restaurant today, then work through the fixes in priority order. Results vary and placements are not guaranteed.