Last updated August 2026.
Every competitor citation in a ChatGPT, Perplexity, or Gemini answer is a slot you did not win. According to G2’s April 2026 survey of 1,076 B2B software buyers, 51% now start software research in an AI chatbot. That is a majority of your pipeline running through answer engines where citation share, not rank position, determines who gets considered.
The problem: most teams know they want to be cited. Few have a systematic way to find the specific prompts where competitors beat them. This piece defines that workflow.
The gap-prompt framework: what it is and why it matters
Gap prompt: any prompt in your tracked set that returns a competitor citation without returning yours.
The concept is simple. The implication is operational. Every gap prompt is a named, repeatable buyer query where an AI engine is actively routing consideration away from your brand. Gap prompts are not a vanity metric. They are a prioritized fix list.
The workflow has four steps:
- Build a baseline prompt set across your category
- Run it across multiple engines and record who appears
- Label every miss as a gap prompt
- Diagnose the source URLs driving competitor citations, then close the gap
The sections below cover each step in detail.
Step 1: Build your baseline prompt set
Start with 30 to 50 prompts that map to real buyer language in your category. Do not write prompts the way you would write a search query. Write them the way a buyer would ask a chatbot.
Good prompt patterns for a baseline:
- Category queries: “What are the best tools for [category]?”
- Comparison queries: “How does [Competitor A] compare to [Competitor B]?”
- Problem queries: “How do I [specific pain point] without [known limitation]?”
- Recommendation queries: “What should a [buyer persona] use for [use case]?”
Run each prompt exactly as a user would type it. Save the full response text, not just a yes/no flag for your brand.
Step 2: Run the prompts and record citation data
Run each prompt across at minimum four engines: ChatGPT, Perplexity, Gemini, and Google AI Overviews. Do not assume results are consistent. They are not.
According to Profound’s analysis of 100,000 prompts across both platforms, only 11% of domains cited by ChatGPT are also cited by Perplexity for the same queries (Profound, July 2025). That means a competitor leading on ChatGPT may be nearly invisible on Perplexity, and vice versa.
For each prompt and each engine, record:
- Which brands appear in the answer (mentioned or linked)
- Which source URLs are cited
- Whether your brand appears and with what framing
- Whether your brand’s information is accurate if it does appear
Doing this manually at scale is not feasible. Platforms built for this work include Profound, Semrush, Otterly.AI, Peec AI, and Temso. See the comparison table below.
Step 3: Label gap prompts and build your competitor citation map
Once you have response data, label every prompt with one of three states:
| State | Definition |
|---|---|
| Win | Your brand appears; no competitor leads the answer |
| Share | Your brand appears alongside one or more competitors |
| Gap | A competitor appears; your brand does not |
Gap prompts are your fix list. Rank them by two criteria:
- Buyer intent. A gap on “best [category] tools for [specific use case]” is more valuable to close than a gap on a broad informational query.
- Gap breadth. A prompt where you miss on three engines is more urgent than one where you miss on one.
Build a competitor citation map alongside this: a table of which competitors appear on which prompt clusters and on which engines. This surfaces not just where you lose but who you lose to, and which engines each competitor has optimized for.
Step 4: Diagnose the source URLs and close the gap
Knowing you miss on a prompt is not enough. You need to know why the competitor wins it.
Pull the source URLs the AI engines cite when they recommend a competitor on your gap prompts. Common patterns:
- Third-party editorial coverage: The competitor was reviewed or recommended on a domain the engine already trusts (G2, industry publications, analyst sites).
- Comparison pages with structured data: The competitor has a dedicated head-to-head comparison page that surfaces cleanly in retrieval. According to AirOps Research (April 2026), comparison pages with three or more tables earn 25.7% more AI citations than those without, for comparison-style queries.
- Direct-answer content: The competitor’s own page opens with a direct answer to the exact query, making it easy for the retrieval layer to extract and cite.
Each pattern has a different fix:
- Third-party gap: Earn coverage on the same trusted domains.
- Comparison page gap: Build or update a dedicated comparison or use-case page with clear direct-answer structure.
- Content gap: Add a direct-answer passage at the top of the most relevant existing page.
Re-run the gap prompt 2 to 4 weeks after publishing. Measure whether citation share moved.
Tools for tracking competitor AI mentions
No single tool is right for every team. The table below compares the five tools named in this piece on the capabilities that matter most for competitive citation tracking.
| Tool | Competitor tracking | Engine coverage | Source URL attribution | Entry price |
|---|---|---|---|---|
| Profound | Share-of-voice by prompt cluster | 9+ engines | Yes, full citation source maps | $99/mo (ChatGPT only); $399/mo full |
| Semrush | AI Toolkit competitor SOV | 5 engines | Partial | $129/mo (Core with AI add-on) |
| Otterly.AI | Weekly competitor comparison reports | 6 engines | No | $29/mo (Lite, 15 prompts) |
| Peec AI | Google AI Overviews volume focus | 5 engines | Partial | Contact for pricing |
| Temso | Gap diagnosis built into workflow | 8 engines | Yes | $89/mo flat |
Profound is the deepest tool for enterprise teams that need to understand citation source patterns in detail. Its visual citation maps show exactly which URLs appear for each prompt cluster, which makes the source-URL diagnosis in Step 4 faster and more precise.
Semrush’s AI Toolkit layers competitor share-of-voice onto an existing SEO workflow, making it a practical choice for teams already running Semrush for traditional SEO.
Otterly.AI delivers automated weekly competitor comparison reports at the lowest price point in the category. The Lite plan at $29/mo is limited to 15 prompts, which is workable for a focused gap-prompt set.
Peec AI has particular depth on Google AI Overviews data, having analyzed 500,000 commercial prompts to establish baseline rates. That makes it useful for teams where AI Overviews is the primary competitive battleground.
Temso is an all-in-one option: it tracks across eight engines, surfaces gap prompts as part of a built-in diagnostic workflow, and connects directly to content execution. For teams that want the tracking, diagnosis, and fix steps in one tool from $89/mo, it covers the full loop without requiring multiple subscriptions.
The cross-engine problem: why you must track all four
One of the most common mistakes in competitor tracking is treating AI engines as interchangeable. They are not.
BrightEdge research found that brands are cited inconsistently across Google AI Overviews, Google AI Mode, and ChatGPT 61.9% of the time. That means a competitor that dominates your category on ChatGPT may have almost no presence on Perplexity, and a gap on Google AI Overviews may not reflect at all in Perplexity results.
A tracking programme that covers only one engine misses the full competitive picture. The minimum viable setup tracks ChatGPT, Perplexity, Gemini, and Google AI Overviews. Coverage of Copilot, Meta AI, and Grok is a meaningful upgrade for categories where those engines have traction.
See the GEO tools ranking for a full breakdown of engine coverage by platform, and the GEO glossary for definitions of citation share, share of model, and related terms.
Common mistakes in competitor citation tracking
Tracking mentions, not source URLs. Knowing a competitor appears is the beginning, not the end. The source URL is what tells you why the engine cited them and what you need to match or replace.
Running prompts once. AI citation behavior shifts as engines update their retrieval pipelines. A prompt set run once is a snapshot. A prompt set run weekly is a trend line.
Ignoring accuracy. Some brands appear in AI answers with inaccurate information. If a competitor is cited but described incorrectly, that is a different kind of gap: an accuracy correction opportunity rather than a content creation one.
Treating all gap prompts equally. A gap on a high-intent, bottom-of-funnel prompt is worth 10x more to close than a gap on a broad awareness query. Sort gap prompts by buyer intent before prioritizing fixes.
What to build once you have your gap prompts
The gap prompt list points directly to the content work. For each cluster of gap prompts:
- If competitors win via third-party coverage: Identify the specific domains the engine cites for those prompts. Build a digital PR list and pitch those outlets.
- If competitors win via comparison pages: Create a dedicated comparison or use-case page with a direct-answer opening and structured tables. Three or more comparison tables in a single page make a meaningful difference to citation rate for head-to-head queries, per AirOps Research.
- If competitors win via owned content: Find the exact page that is being cited. Read its opening paragraph. Write a version on your domain that answers the same question more directly and with higher factual density.
This is the core loop of competitive GEO: track gap prompts, diagnose source URLs, close the gap with content or coverage, and measure the result.
Next steps
If you are starting from zero, the fastest path to a working gap-prompt list is:
- Write 30 prompts using buyer-intent language from your category.
- Run them across ChatGPT, Perplexity, Gemini, and Google AI Overviews.
- Record who appears and which URLs are cited.
- Label every miss as a gap prompt.
- Sort by buyer intent and start with the five highest-priority gaps.
The GEO tools ranking at /rankings/geo-tools covers every platform that can automate Steps 2 and 3, with verified pricing and engine coverage for each. The methodology page at /methodology explains how citation share is measured and what counts as a credible tracking signal.
Start with a manual baseline. Then choose a platform that fits your budget and automates the recurring measurement. The gap prompts will tell you exactly where to focus.