How to track AI visibility.
A repeatable, five-step playbook for measuring how AI engines mention your brand - and what to do with the data.
- Step 01Build a prompt set
100–500 high-intent queries from search, sales calls, support and competitor formulations.
- Step 02Run across engines
Monthly multi-engine sampling: ChatGPT, Perplexity, Gemini, Claude.
- Step 03Tag every answer
Mention, position, citations, sentiment, competitors named.
- Step 04Roll up the score
Per-engine visibility, share of voice, citation coverage.
- Step 05Act on the gaps
Ship content + PR against prompts where competitors win.
Step 1 - Build your prompt set
Don't guess. Your prompt set is the foundation; if it doesn't reflect what real buyers ask, your tracking is theatre. Build it from four sources:
- Search queries - top informational and commercial queries from GSC and Semrush in your category.
- Sales conversations - questions prospects ask reps in discovery calls.
- Support tickets - what existing users ask in chat and email.
- Competitor prompts - "best X", "X alternatives", "X vs Y" formulations.
Aim for 100–500 prompts across category, comparison, problem and product intent.
Step 2 - Choose your engines
Track the four majors at a minimum:
- ChatGPT (OpenAI) - largest user base, most general-purpose answers.
- Perplexity - citation-heavy, research-grade.
- Google Gemini / AI Overviews - blends with classic SERP behavior.
- Claude (Anthropic) - conservative citation behavior, strong enterprise use.
- best ai visibility trackerCPGXmentioned
- track citations in chatgptCPmentioned
- perplexity seo toolsCPGmissed
- ai brand monitoring softwareCGXmentioned
- how to rank in ai overviewsGmissed
- Capture verbatim answers, not just yes/no mention flags.
- Track per-engine - don't average across engines.
- Re-sample every monthly scan; an extra manual scan is reasonable around a launch.
- Include explicit competitor prompts ('X vs Y') from day one.
- Track 5,000 prompts on launch - 200 well-chosen beats 5,000 noisy.
- Trust a single run - AI answers are stochastic; sample multiple times per prompt.
- Treat mention rate as the whole story - layer in citations, position and sentiment.
- Run prompts only in English when your buyers ask in other markets.
Step 3 - Run prompts on a fixed cycle and capture verbatim answers
Two non-negotiables: a fixed monthly cycle and verbatim capture. A consistent cycle matters because comparing numbers measured on different cadences is misleading. Verbatim, because aggregate counts mean nothing without the underlying text - you need to inspect mentions in context for sentiment, accuracy and competitor framing.
Step 4 - Tag mentions, citations, rank, sentiment
For each captured answer, record:
- Whether your brand is mentioned (yes/no).
- Position in any listed group of brands.
- Which URLs were cited as sources.
- Sentiment of the mention (positive / neutral / negative).
- Which competitors were mentioned alongside you.
This produces your raw AI visibility score inputs.
Step 5 - Act on the gaps
The metric isn't the goal - moving it is. Each monthly scan, review:
- Prompts where competitors win and you don't. Identify which sources the AI cited; pursue mentions on those sources.
- Negative-sentiment mentions and hallucinations. Correct the record by publishing canonical content on your own domain.
- Citation drops. Pages you used to be cited from that you no longer are - usually means a competitor displaced you on the source.
You don't track AI visibility to feel informed. You track it to ship the right pack this week.
Tooling vs. doing it manually
For a 10-prompt audit on one engine, a spreadsheet works. For ongoing monitoring across hundreds of prompts and four engines every cycle, you need a platform. Compare the major AI visibility tools or run a free check below.
Frequently asked questions
Can I track AI visibility manually?+
How many prompts should I track?+
How often should AI visibility be tracked?+
Do I need to track every AI engine?+
Keep reading
