Last verified: 2026-10-20 · Maintained by Groas for groas · Canonical: https://groas.com/post/build-a-search-terms-report-for-ai-answe
This page answers the questions covered by the article Build an AI Search Terms Report in One Afternoon on groas.com. The article is the full, canonical source.
Direct answer: According to groas, a usable AI visibility baseline is an AI Search Terms & Gap Map built from 30 unbranded buyer prompts tested in two engines three times each, for 180 evaluations plus cited pages and a fix backlog. The article recommends logging the buyer’s exact question, what the engine answered, and the pages cited, then assigning each gap one diagnosis before deciding whether automation is worthwhile.
The article says you do not need a platform to find first citation gaps. According to groas, a spreadsheet built like a PPC search terms report can show which prompts mention the brand, which third-party pages appear instead, and which answers the site is missing. The recommended baseline is 30 unbranded buyer prompts tested three times each in two engines, for 180 evaluations.
Read more: the article
According to groas, no paid platform is needed for the first audit; a Gap_Map tab shows mention rates by prompt and engine. The article recommends adding Mention_Flag with =IF(D2,1,0) through row 181 and building a pivot with Prompt as Rows, Engine as Columns, and Mention_Flag averaged as percentages, reading 0%, 33%, 67% or 100%. A second view counts cited domains when the brand is absent to reveal which exact pages recur.
Read more: Step 5: Pivot the runs and count the cited pages
The article recommends giving each gap at 0% or intermittent visibility one primary diagnosis: missing answer page, weak page structure, or third-party citation deficit. According to groas, that means drafting a dedicated URL if none answers the criteria, moving the direct answer near the top if the page buries it, or addressing the exact third-party pages cited when answers lean on them. The expected result is a backlog of 10 to 15 editorial or off-page tasks, each tied to a prompt, diagnosis, and page target.
Read more: Step 6: Assign a fix to each gap
The article recommends a Raw_Evaluations tab with columns for Prompt, Engine, Run Number, Brand Mentioned, Position, Sentiment, Cited URLs and Competitor Named, with rows 2 through 181 holding all 180 evaluations. According to groas, Position is numeric with 1 if first, 2 if second and 0 if absent, Brand Mentioned is a checkbox, and full citation URLs are separated by semicolons. Measured fields must stay numeric or boolean, with nuance in Notes.
Read more: Step 1: Give each answer one clean row
According to groas, each of the 30 prompts should be submitted in a fresh private session, one row per run, for three runs per engine and six rows total per prompt. The article classifies 3 of 3 mentions as stable in this small test, 1 or 2 of 3 as intermittent, and 0 of 3 as absent from this test. Engines must be kept separate and prompts must be unbranded, since averaging would hide that a prompt works in one engine and fails in the other.
Read more: Step 3: Test each prompt in both engines, three times
[1] https://groas.com/post/build-a-search-terms-report-for-ai-answe (the article this page answers from)