How To Identify Your Target Prompts for LLM Visibility Monitoring

by Chris Byrne (published 20.11.25 - updated 4.12.25)

   Note that not all LLM visibility monitoring tools have an identified "prompter's location" (e.g. United Kingdom - you may not be able to specify an exact location e.g. London either) in the project settings from what I see . In my research I have seen that Google AI Overviews are localised for some prompts e.g. the results for "aluminium window manufacturers" are different in Glasgow to those in Hampshire . This is also the case in Chatgpt even for some prompts without any (apparent) implicit local intent e.g. “best campervans” - this may be dependent on your (personalisation) settings in the LLM / search engine . For "normal" Google ranking tracking one may need to simulate searches from multiple specific locations (e.g. London / Bristol UK etc) and the same may be true for full LLM visibility monitoring...

   Ever wonder how LLM visibility checker tools suggest prompts a user should be tracking? It is possible they use some of the techniques outlined below:

1.   It is possible to use Chatgpt , Google Gemini / Keyword Planner to generate a list of “plausible completions” in Chatgpt / Google Gemini (and AI Overviews) that might mention a product / brand or website (without the aforementioned brand or website being mentioned in the prompt) to identify your target prompts for LLM visibility monitoring using Chatgpt / Google Keyword Planner. One can even ask the tools to specify a location in the prompt if relevant. Follow the 3 steps below
   
   

1. Put a relevant prompt (or list of prompts) e.g. “best smartphones” in Google Keyword Planner to get similar searches (using many popular SEO tools to get this list is only guesswork as they don't use Keyword Planner data) - this is arguably the best search data for Google dominated markets e.g. the UK. It's worth noting that people have been using GOOG as an answer engine as well for a while now (asking it questions) so we should be able to see the types of questions people might also ask in LLMs - sometimes these questions are truncated e.g. “best smartphones” instead of “what are the best smartphones” . See an example of Google Keyword Planner USA monthly search volume data below:

2. Then also prompt Google Gemini as follows: for example:

“give me a list of prompts where the Apple Iphone could be part of a plausible completion in Google Gemini without being named in the prompt. Give a semantic cue matrix showing why each prompt is likely to invoke these completions. give results in table with

Trigger type (category, feature, context, etc.)

Example trigger cue Strength (Strong, Medium, Weak)

Probability tier (how likely the brand appears in a completion)

Example prompt”

For CGPT (note a variation on the above prompt appears also to work sometimes!): 

"provide semantic cue tables /  likelihood categories / example prompts / domain-relevance matrices for prompts without mentioning the brand name in the prompt that would mention Apple Iphone in the responses"

  You can also do the same for your competitors and compare / contrast as part of one prompt. It appears from what I'm seeing some LLM visibility tools may do something similar to suggest prompts a user should be tracking.

    It is also possible to try to elicit prompts that would mention a brand / product but not another: "provide semantic cue tables /  likelihood categories / example prompts / domain-relevance matrices for prompts that without mentioning the brand name in the prompt would mention Apple Iphone in the responses but not Samsung S Series"

  3. Then filter / edit (as appropriate) & merge the two lists you have generated in one and create a project in your LLM Visibility Monitoring using these prompts. You can also do the same for your competitors and compare / contrast.