Checking the evidence
How to measure AI visibility without a made-up rank
Record what appeared, where it appeared and how you checked. Keep brand mentions, links to your site and business enquiries separate. A repeated, defined sample can describe that sample; one screenshot cannot establish your visibility across an entire market.
Does a mention count as a citation?
A business name appearing in an answer is a mention. A source link is a citation to a page. OpenAI explains how to open citations and source links in ChatGPT Search answers. Follow the link and check whether the destination supports the statement.
For the register below, count a link to your own website as an owned-site citation. Record a directory, publisher or other third-party page separately. These are reporting definitions: a citation is not proof that its statement is correct or that it generated an enquiry.
Why repeat the check?
In SparkToro's January 2026 study, repeated brand-recommendation prompts produced varying lists and orderings. The researchers found appearance frequency more informative than treating answer order as a stable rank. The study's prompt selection and provider context limit how broadly its findings apply. Read the study and its methodology.
For your own checks, save the exact question, engine, date, market context and response. Repeat the same version before comparing periods. Changing the question while drawing a continuous trend hides a method change.
What should the sample register contain?
| Record | Why it matters |
|---|---|
| Prompt and buyer intent | Shows what question was tested and whether it matches a real need |
| Engine, mode, locale and time | Distinguishes consumer search, API output and changing conditions |
| Attempted, valid and failed runs | Makes the denominator inspectable |
| Brand mention and owned-site citation | Separates a name appearing from a link to your evidence |
| Raw response and source URL | Lets someone verify the interpretation |
A worked example
Illustrative numbers: suppose 30 valid answers include six mentions of a business, and three of those answers link to its website. The mention rate is 6/30, or 20%. The owned-site citation rate is 3/30, or 10%.
Those figures describe the valid answers in that test. They do not mean 20% of customers saw the brand, or that 10% of enquiries came from AI. Report failed attempts separately and explain what counted as a valid response. Small, correlated prompt samples cannot support confident market-wide claims.
What can Google report directly?
Google announced dedicated generative-AI performance reports in June 2026 and noted worldwide rollout on 31 August. These report impressions and supporting breakdowns for Google's AI features. The data also appears in the overall performance report, so do not add it to the total a second time. Google's report announcement.
Keep that provider-reported view separate from third-party prompt experiments. If your reporting integration cannot retrieve the dedicated report, show that limitation instead of inventing a substitute.
Does your site need an llms.txt file?
Not for Google Search rankings. Google's 15 June 2026 clarification says it ignores these files for search visibility and rankings, including its generative features. Maintaining one for another system is fine, but its presence is not an AI visibility result. Read Google's guidance and the dated documentation update.
The llms.txt proposal describes a concise file that helps agents use a website. That is a different purpose from measuring whether an answer mentions or cites your business. A file can be useful to a particular documentation tool or agent without affecting Google's ranking systems.
What does provider adoption actually show?
OpenAI's developer documentation links to its own llms.txt index. That verifies a documentation use case. It does not demonstrate that publishing the file on your business website makes ChatGPT Search recommend you. For search access, OpenAI documents OAI-SearchBot separately from GPTBot, its training crawler. OpenAI's crawler and documentation page.
Ask which named system will consume the file and what task it helps that system complete. A provider publishing its own index is different evidence from a provider documenting that it uses other websites' indexes for search. Treat any claimed citation uplift as something requiring a disclosed test.
Why does Lighthouse mention it?
Lighthouse includes an llms.txt check under agentic browsing. Its documentation says a missing file returning HTTP 404 is marked not applicable because the file is optional; a server error retrieving the file is flagged. This checks a browsing facility, not a Google ranking benefit. Lighthouse's llms.txt audit.
A practical decision for your website
- Named consumer and useful task: consider a short, accurate index when a tool you use documents support for it, and check its links when the underlying pages change.
- No clear use case: prioritise accessible public pages, useful answers and the relevant provider's documented search controls.
- Already have a file: record it as a maintained technical asset. Continue to measure mentions, owned-site citations and enquiries separately.
If an agency lists llms.txt as a deliverable, ask what it does, how they verified that use and how it will be kept current. Do not count creating the file as a citation or as proof of increased visibility.
Where do enquiries fit?
Track a separate enquiry record with landing page, referrer or campaign tags when available, customer-reported discovery and qualification outcome. Missing attribution stays unknown. An AI citation is evidence of appearance in an answer, not proof that it caused a sale.
What should you ask an agency to show?
Open one observation behind the chart. Check the saved question and answer, follow the citation, and compare the date and method with the previous period. Then ask which practical decision the result supports. If the agency cannot open the underlying evidence, the chart is not ready to guide spending.
Sources and review
- OpenAI ChatGPT Search help. How source citations and links appear in search answers.
- SparkToro AI consistency study. Repeated-prompt observations and limitations, 27 January 2026.
- Google generative-AI performance report announcement. Provider reporting, updated 31 August 2026.
- Google AI optimisation guide. Google-specific treatment of llms.txt.
- Google documentation updates. 15 June 2026 clarification.
- llms.txt proposal. Proposed agent-facing information format.
- Lighthouse llms.txt audit. Optional agentic browsing check and HTTP status handling.
- OpenAI crawler documentation. Search and training controls; own documentation index.
References checked on . Provider guidance can change. Our checklists and illustrative examples are recommendations, not observed client results. Send a correction.
Revision notes
- 9 September 2026: added the llms.txt decision guide, Google-specific ranking guidance, documentation adoption limits and the Lighthouse audit distinction.
- 6 September 2026: clarified mentions, owned-site citations and third-party sources, with OpenAI Search documentation. No client results added.
- 6 September 2026: initial researched version.