What is LLM rank tracking?

LLM rank tracking is the practice of asking a fixed set of questions to large language model engines on a schedule and recording whether, and in what position, each engine names your product. It borrows its name from SEO rank tracking, where a tool checks where your page sits in search results for each keyword. The LLM version swaps keywords for buying questions and swaps a results page for a written answer.

How LLM rank tracking differs from SEO rank tracking

Search results are a stable list, so a position of seven means something. AI answers are prose, and an engine may name three products in a sentence, name none, or name a different three on the next run. So LLM rank tracking records a rate rather than a rank: across many asks, how often were you named, how often first, and who else appeared. That rate is share of model. It also records citations, because the sources an engine cites explain the names it gives.

What to record when tracking LLM rankings

Start with a prompt set of ten to thirty questions a buyer types, not your brand name. Ask each engine separately, since ChatGPT, Perplexity, Gemini, Grok, and Google AI draw on different sources and refresh at different times. For every answer log whether you were named, the order of names, the competitors named, and the cited pages. Run weekly. Oetzi's share of model check does this across the five engines for two credits per question and lists the cited threads next to the score.

What to do with the results

The naming rate is the headline. The cited sources are the work. If an engine names a competitor and cites a Reddit thread, that thread is where a helpful, disclosed reply changes the next answer. If it cites a stale comparison, that comparison needs a correction. Tracking without acting is a dashboard. The engines pages describe how each engine sources its answers.

Questions

How often should LLM rank tracking run?

Weekly is enough for most products. Engine answers shift when models refresh, when new threads rank, or when a competitor launches, and none of those happen daily. Run daily only around a launch, a pricing change, or a comparison thread you just answered, when you want to see the effect quickly.

Why do LLM answers change between runs?

Generation is probabilistic, and engines that search pull different pages each time. A single ask is a sample, not a measurement. That is why tracking counts a rate across repeated asks rather than treating one answer as the ranking. Ten asks per question per engine gives a stable enough read.

Which engines should I track?

Track where your buyers ask. For most software categories that is ChatGPT first, then Perplexity and Google AI Overviews for their citations, then Gemini and Grok. Tracking all five costs little more than tracking one and shows which engine is the outlier when a name appears or disappears.