GEO Audit Template from AI Answers to Evidence Gaps

This template helps you systematically record AI answers, identify evidence gaps, and prioritize fixes.

Define Your Query Set and Scope

Start by defining a focused set of queries that reflect how your buyers actually ask about your category. For a B2B product, include queries that target product specifications (e. g. , "[product type] with [specific feature]"), certifications (e. g.

, "[industry] compliance standards"), and case studies (e. g. , "[use case] implementation results"). Also include comparison queries (e. g. , "[your product] vs. [competitor]") and problem-solving queries (e. g. , "how to solve [common pain point]").

Aim for 20–50 queries per audit cycle, balancing high-intent commercial terms with informational ones.

Select the AI platforms you want to monitor. Common choices include ChatGPT, Perplexity, and Google AI Overviews, but you may also include Bing Chat or other generative engines relevant to your audience.

For each platform, note the interface version and date, as AI outputs can change frequently. Record the region you are simulating (e. g. , US, UK, EU) because answers may vary by locale due to language, data availability, or legal constraints.

If you target multiple regions, run the same query set in each region and treat them as separate audit entries.

Document your scope decisions in a simple spreadsheet: list each query, platform, and region combination. This becomes your audit matrix. For example, if you have 30 queries, 3 platforms, and 2 regions, you will have 180 data points.

That may be too many for a manual audit, so prioritize: start with your top 10 commercial queries on the most influential platform for your market, then expand. Define a regular cadence—monthly or quarterly—to track changes over time.

Record AI Answers: Fields and Instructions

When you run each query, capture the full AI answer text exactly as displayed. Do not paraphrase or truncate. Use a consistent naming convention for screenshots: include date, platform, region, and query ID (e. g. , 2025-05-01_ChatGPT_US_Q01).

Store screenshots in a shared drive with a folder structure mirroring your audit matrix.

For each answer, record the following fields in your audit table:

– **Query**: The exact query text.
– **Platform**: The AI tool used.
– **Region**: The simulated geographic region.
– **AI Answer Text**: Paste the full response.
– **Brand Mention**: Classify as "Yes" if your brand is explicitly named, "No" if not mentioned, or "Indirect" if only a product category or generic description appears that could imply your brand but does not name it.
– **Cited URL**: List any URLs the AI cites in its answer. If none, leave blank.
– **Factual Error**: Mark "Yes" if you spot a factual inaccuracy (e.g., wrong product spec, outdated certification), otherwise "No".
– **On-site Coverage**: Mark "Yes" if your own website has a page that directly answers the query, otherwise "No".
– **Third-party Evidence**: Mark "Yes" if there is independent, authoritative content (e.g., industry reports, news articles) that supports your claims, otherwise "No".
– **Repair Priority**: Assign High, Medium, or Low based on the criteria below.

To ensure consistency, define what counts as a factual error. For example, if the AI states your product has a feature it does not, that is a clear error. If it cites an outdated price or specification, that is also an error.

If the AI provides a general answer that is not wrong but omits your brand, that is not a factual error but a brand mention gap.

Identify Evidence Gaps: Cited URLs and Factual Errors

After recording answers, analyze the cited URLs. Check whether each cited URL is official (your own domain or a recognized authority like a government site or industry body) or third-party (news, blogs, forums).

If the AI cites a URL that is not authoritative or is broken, flag it as a potential issue. For example, if it cites a competitor’s page when your page would be more relevant, that indicates an on-site coverage gap.

Differentiate between two types of evidence gaps: missing on-site coverage and missing third-party evidence. On-site coverage means your website lacks a page that directly answers the query.

Third-party evidence means there is no independent source that validates your claims. Both can affect whether an AI cites you, but they require different fixes. On-site gaps require creating or optimizing content on your domain.

Third-party gaps require earning mentions from reputable external sources.

Prioritize repairs using a simple rule: High priority if the AI answer contains a factual error about your brand or product, or if the AI does not mention your brand for a high-intent commercial query where you have strong on-site coverage.

Medium priority if the AI mentions a competitor but not you, or if the cited URL is outdated or non-authoritative. Low priority if the answer is generic and your brand is not expected, or if the gap is only in third-party evidence for a low-intent query.

Use the following fillable audit table to record your findings. Duplicate this table for each query–platform–region combination.

| Query | Platform | Region | AI Answer Text | Brand Mention (Yes/No/Indirect) | Cited URL | Factual Error (Yes/No) | On-site Coverage (Yes/No) | Third-party Evidence (Yes/No) | Repair Priority (High/Medium/Low) |
|——-|———-|——–|—————-|——————————–|———–|————————|—————————|——————————-|———————————-|
| | | | | | | | | | |
| | | | | | | | | | |

Audit On-Site Coverage

Before you can decide whether an AI answer’s lack of brand mention is a content gap or a citation gap, you need to know what your own site actually offers.

On-site coverage is the baseline: if the information exists but is not structured or fresh, the fix is different from a situation where the content is missing entirely.

Start by listing every query from your audit set. For each query, ask: does our site have a page that directly answers the question? This is not about whether the page ranks in traditional search; it is about whether the content exists and is crawlable.

A product page that mentions a feature in passing is not coverage. Coverage means the page’s primary intent matches the query’s intent.

For example, if the query is “how to export data from platform X,” a help center article titled “Exporting Your Data” is coverage; a blog post that mentions export in one paragraph is not.

Next, assess structured data. Schema. org markup helps search engines and AI systems understand the entity and its attributes. For a product page, that might be Product schema with offers, reviews, and aggregate ratings.

For an FAQ page, FAQPage schema can make questions and answers explicit. But do not assume that adding schema guarantees citation. Google’s documentation on AI features states that standard SEO foundations apply, but eligibility does not guarantee appearance.

Treat schema as a necessary but not sufficient condition.

Record content freshness. AI systems may favor recent information, especially for queries about current events, pricing, or product features. Note the last update date of each relevant page.

If a page is over a year old and the topic is time-sensitive, that is a potential gap. Also check for duplicate or thin content. If you have multiple pages covering the same query, consolidate them to avoid confusion.

Finally, verify crawlability. Use a tool like Google Search Console’s URL Inspection or a simple site: search to see if the page is indexed. If it is not indexed, no amount of on-site optimization will help. Check robots. txt and meta robots tags.

Record the indexed status in your audit table.

Audit Third-Party Evidence

On-site coverage is only half the picture. AI answers often cite third-party sources—industry publications, news sites, academic papers, or review platforms. If your brand is not mentioned by independent sources, you are missing a key evidence layer.

For each query, identify which third-party domains appear in the AI answer’s citations. List them in your audit table. Then check whether your brand is mentioned on those domains. A mention could be a direct reference, a quote, or a case study.

If the mention exists, note the URL and the date of publication. If it does not exist, that is a gap.

Assess the authority of those third-party sources. Authority is not a fixed metric; it depends on the niche and the query. For a B2B software query, a mention in a well-known industry blog may carry more weight than a generic news aggregator.

For a medical query, academic sources are critical. Use your judgment, but be consistent. Record the source type (news, academic, industry blog, etc. ) and a rough authority score (high, medium, low) based on domain reputation and editorial standards.

Timeliness matters. A third-party article from three years ago may be outdated for a fast-moving topic. Check the publication date and note whether the information is still accurate.

If the third-party content contains factual errors about your brand, that is a high-priority issue.

Also consider the relationship between on-site and third-party content. If your on-site page is authoritative but no third party links to it, that is a gap.

Conversely, if third parties mention you but your own site lacks a corresponding page, the AI answer may cite the third party but not your brand. Both scenarios require different fixes.

Prioritize Fixes: From Gaps to Action

Once you have completed the audit for a set of queries, you will have a table full of observations. The next step is to prioritize repairs. Not all gaps are equal. Use the following rules to allocate resources.

**High priority:** Any query where the AI answer contains a factual error about your brand, or where your brand is not mentioned at all despite being a relevant answer.

Factual errors can damage trust and may require immediate correction, both on your site and through outreach to the AI provider if possible. No brand mention means you are invisible in that answer. Fix these first.

**Medium priority:** Queries where your brand is mentioned but the citation is non-authoritative, or where you have on-site coverage but no third-party evidence.

For example, if the AI answer cites a random blog with low authority, you need to build third-party mentions from reputable sources. If you have a great on-site page but no external validation, focus on earning links or mentions.

**Low priority:** Queries where you have both on-site coverage and third-party evidence, but the content is slightly outdated or could be improved. These are incremental updates.

Also low priority are queries where the AI answer is correct and cites your brand, but you want to improve the depth of the answer. Do not spend time on these until high and medium priorities are resolved.

To make this actionable, use the fillable audit table below. For each query, record the platform (e. g. , ChatGPT, Perplexity, Bing AI), region (e. g.

, US, UK, Germany), the AI answer text, whether your brand is mentioned, the cited URL, whether there is a factual error, whether you have on-site coverage, whether you have third-party evidence, and your repair priority.

This table is a working document; update it as you make changes and re-run the queries.

Remember that this audit is a snapshot in time. AI answers change frequently. Re-run your query set on a regular basis—monthly or quarterly—and track changes. Do not assume that a fix will permanently solve a gap. The evidence landscape is dynamic.

When you identify a factual error, document it carefully. Note the exact wording of the AI answer, the correct information, and the source that supports the correction.

This documentation is useful if you decide to contact the AI provider or publish a correction on your own site.

Finally, keep your audit focused on evidence gaps, not on trying to game the system. The goal is to ensure that when an AI system answers a question, it has access to accurate, authoritative information about your brand.

That is a long-term strategy, not a quick fix.

Next step

Download the fillable audit table and start with your first query set.