The 34 GEO Checks That Determine If AI Will Cite Your Content

AI search engines do not pick citation sources at random. They evaluate content against specific criteria: Is it well-structured? Does it directly answer the question? Is it backed by data? Can the source be trusted? Here are the 34 checks that matter, organized into five categories.

The Scoring Framework: Five Categories

Every page can earn up to 100 points, distributed across five categories that reflect what AI models prioritize when selecting citation sources:

• Structure (25 points, 13 checks): The technical foundation that makes your content parseable and extractable.
• Relevance (25 points, 7 checks): How well your content matches the query and delivers clear answers.
• Depth (15 points, 4 checks): The level of detail, data, and comprehensive coverage you provide.
• Trust (15 points, 5 checks): Credibility signals that tell AI models your content is reliable.
• Citability (20 points, 3 checks): Whether AI can extract self-contained, data-rich passages to use as direct citations.

Each check within these categories is binary: pass or fail. There is no partial credit and no subjective interpretation. Either your page has a descriptive H1 tag, or it does not. Either you include specific statistics, or you do not. This approach eliminates the variance that plagues traditional SEO scoring tools.

Category 1: Structure (25 Points, 13 Checks)

Structure checks verify that your content is technically sound and easy for AI models to parse. These are the building blocks that make extraction possible.

HTML and Content Organization

• H1 tag present (2 pts): Every page needs exactly one H1 that clearly states the topic. This is the primary signal AI models use to understand what the page is about.
• Meta description present (2 pts): A well-written meta description gives AI models a concise summary to work with. Keep it under 160 characters and make it descriptive.
• H2 subheadings (2 pts): Multiple H2 tags create a scannable outline. AI models use these to navigate long content and find specific sections relevant to a query.
• Heading hierarchy (2 pts): Proper heading nesting (H1 → H2 → H3) helps AI models parse your content outline. Skipped levels signal disorganized content.
• Images present (2 pts): Pages with relevant images signal comprehensive coverage. Visual content also provides additional context for multimodal AI systems.
• Alt text on images (2 pts): Descriptive alt text helps AI models understand what images show, reinforcing the topical relevance of your content.
• Multi-modal content (2 pts): Pages with multiple content types (images plus tables or video) get significantly more AI attention than text-only pages.

Schema and Metadata

• FAQ section (2 pts): An FAQ section is one of the strongest GEO signals. It gives AI models pre-formatted question-answer pairs they can cite directly.
• Schema markup (2 pts): JSON-LD structured data helps AI understand the content type and extract structured information.
• Schema type appropriate (2 pts): The schema types used should match the page topic. An article about recipes should use Recipe schema, not just generic Article.
• Canonical URL (2 pts): Tells AI models which version of the page is authoritative, preventing duplicate content confusion.
• Open Graph tags (1 pt): OG tags provide AI with structured metadata about your content, including title, description, and image.
• Internal links (2 pts): Links to related content on your site demonstrate topical authority and depth of coverage.

Category 2: Relevance (25 Points, 7 Checks)

Relevance checks measure how well your content aligns with user queries and delivers clear, direct answers.

• H1 topic alignment (4 pts): Your H1 should directly reflect the primary topic. Generic or vague H1 tags reduce your citation potential.
• Meta description topic alignment (4 pts): The meta description should reinforce the H1 topic and include key terms that AI models associate with the query.
• First paragraph relevance (4 pts): The opening paragraph should provide a direct answer or clear thesis statement. AI models heavily weight early content.
• Direct answers present (4 pts): At least one clear, concise answer to the implied question. This is what AI models extract and cite.
• H2 topic coverage (3 pts): Subheadings should cover the key aspects of the topic in a logical order. This demonstrates comprehensive, organized coverage.
• Entity coverage (3 pts): Content should mention the key entities (people, products, concepts, places) that are relevant to the topic. AI models use entity recognition to gauge completeness.
• Content clarity (3 pts): Clear, jargon-free writing that communicates ideas efficiently. AI models prefer content that can be cited without additional explanation.

Category 3: Depth (15 Points, 4 Checks)

Depth checks evaluate whether your content goes beyond surface-level coverage. AI models prefer sources that provide specific, verifiable information with sufficient substance.

• Minimum word count (3 pts): Content should be at least 500 words. Thin pages with insufficient text rarely get cited by AI models, which need enough material to extract meaningful answers.
• Facts and statistics (4 pts): Specific numbers, percentages, research findings, and data points. AI models strongly prefer citable facts over vague claims. A page that says "traffic has declined significantly" is less useful than one that says "organic click-through rates dropped 7-13% in 2025."
• Comprehensive coverage (4 pts): The page should cover all major aspects of the topic thoroughly. Thin content that touches on a topic without depth rarely gets cited.
• Subtopic completeness (4 pts): Content should cover the key subtopics with no major gaps. If you are writing about "email marketing," you should cover list building, deliverability, segmentation, and metrics — not just one aspect.

Category 4: Trust (15 Points, 5 Checks)

Trust checks assess whether your content comes from a credible source and meets quality standards that AI models use to filter reliable information from noise.

• Author attribution (3 pts): A visible author name signals accountability and expertise. AI models factor this into trust assessments. Author bios, credentials, and professional links all contribute.
• Publication date (2 pts): Date stamps help AI models assess freshness and determine if the information is current. Note: this check is intelligent — homepages and non-article pages are not penalized for missing dates.
• Content freshness (3 pts): Is the content current? Are statistics and references up to date? Content updated within 13 weeks gets the most AI citations.
• External citations (3 pts): Outbound links to credible sources show your content is well-researched and verifiable. References to official reports or recognized authorities boost credibility.
• E-E-A-T signals (4 pts): Experience, Expertise, Authoritativeness, and Trustworthiness. These Google quality guidelines are also used by AI models to assess source credibility.

Category 5: Citability (20 Points, 3 Checks)

Citability checks evaluate whether AI can actually extract and use your content as a direct citation. This is the differentiator — a page can be well-structured and relevant but still not get cited if the content is not extractable.

• Answer-first structure (7 pts): For each H2 section, the first 1-2 sentences should directly answer the question implied by the heading. AI models extract the opening of sections — if you bury the answer after preamble, AI skips your content and cites a competitor who leads with the answer.
• Data and statistic density (7 pts): Content should have at least 1 specific data point per 300 words — percentages, dollar amounts, named studies, dated statistics, named benchmarks. AI models prefer sources with concrete, verifiable data they can quote directly.
• Content extractability (6 pts): Your content should contain at least 3 self-contained passages (100-200 words each) that fully answer a specific question without needing surrounding context. These are the exact passages AI will pull into its responses.

Why Pass/Fail Beats Subjective Scoring

Traditional SEO tools assign scores based on proprietary algorithms that factor in dozens of weighted variables. Run the same page through the same tool twice, and you might get different results. This makes it nearly impossible to know whether your changes actually improved anything.

The 34-check approach is different. Each check has a specific, verifiable condition. Either your page has an H1 tag or it does not. Either your first paragraph contains a direct answer or it does not. The score is deterministic: the same page always produces the same score.

This means you can make a change, re-analyze, and see exactly which checks flipped from fail to pass. No ambiguity, no variance, no guessing.

A Real Example: From 48 to 85

Consider a typical blog post that scores 48 out of 100 on its first analysis. The report shows it is failing on: no meta description, no FAQ schema, no alt text on images, no author attribution, no publication date, weak first paragraph, and no direct answers.

The fixes are specific and actionable:

• Add a descriptive meta description (+2 pts structure + 4 pts relevance)
• Add FAQ section with three questions (+2 pts structure)
• Add alt text to all images (+2 pts)
• Add author name and bio (+3 pts)
• Add publication date (+2 pts)
• Rewrite the first paragraph with a direct answer (+4 pts)
• Add statistics and data points (+4 pts)
• Add external citations (+3 pts)

Total improvement: 26 points, bringing the page from 48 to 74. Each fix maps to a specific check, and each check contributes a known number of points. There is no mystery about what moved the score.