LLM ranking factors are the signals that determine whether AI models — ChatGPT, Gemini, Perplexity — retrieve, prioritise, and cite your content. The 12 key factors are: structured data (JSON-LD), direct answer presence, heading hierarchy, entity clarity, content freshness, topical authority, table and list usage, FAQ sections, internal link density, meta description quality, domain authority, and content depth. Unlike traditional SEO, LLMs weight extraction ease and structured markup far more heavily than backlinks or page speed.
AI models have their own ranking system. It overlaps with Google's but diverges in critical ways. The biggest difference: LLMs prioritise content they can easily extract facts from. Structured data, direct answers, and tables are the new backlinks.
| # | Factor | Impact | You control it? |
|---|---|---|---|
| 1 | Structured data (JSON-LD) | Very high | Yes |
| 2 | Direct answer presence | Very high | Yes |
| 3 | Heading hierarchy (H1-H3) | High | Yes |
| 4 | Entity clarity | High | Yes |
| 5 | Content freshness | High | Yes |
| 6 | Topical authority | High | Partially |
| 7 | Tables and lists | High | Yes |
| 8 | FAQ sections | Medium-high | Yes |
| 9 | Internal link density | Medium | Yes |
| 10 | Meta description quality | Medium | Yes |
| 11 | Domain authority | Medium | Partially |
| 12 | Content depth | Medium | Yes |
| Concept | Definition | Why it matters |
|---|---|---|
| LLM ranking factor | A signal that influences whether an AI model retrieves and cites your content | Understanding factors lets you optimise systematically instead of guessing |
| Extraction ease | How simple it is for an AI to pull a quotable fact from your page | The single biggest differentiator between SEO ranking and LLM ranking |
| Topical authority | Depth and breadth of content coverage on a specific subject | Models prefer citing sources that cover a topic comprehensively |
| Entity clarity | How unambiguously your brand or topic is defined on the page | Reduces attribution confusion — the model confidently names your brand |
Each factor explained
1. Structured data (JSON-LD)
Schema.org markup — Article, FAQPage, HowTo, Product, Organization — provides machine-readable metadata that tells the model exactly what your page is about. It is the most reliable way to ensure correct entity attribution and topic classification.
Action: Add JSON-LD to every important page. Start with Organization on your homepage and Article + FAQPage on content pages.
2. Direct answer presence
A concise, factual statement in the first 200 words that directly answers the topic question. LLMs scan the top of the page first and often quote the first clear answer they find.
Action: Add a 1-2 sentence definition or summary before your introduction. This is the "Direct Answer" block checked by a GEO Content Audit.
3. Heading hierarchy
A clean H1 > H2 > H3 structure with descriptive, question-like headings helps the model navigate your page. It acts as a table of contents that the AI uses to jump to the most relevant section.
Action: Use one H1 (the page title), 4-8 H2s (major sections), and H3s for subsections. Phrase headings as questions when appropriate ("How does X work?").
4. Entity clarity
Consistent, unambiguous naming of your brand, product, or topic throughout the page. If you refer to the same thing by three different names, the model may not connect them.
Action: Pick one canonical name and use it consistently. Reinforce it with JSON-LD Organization or Product schema.
5. Content freshness
AI retrieval systems factor in when a page was last updated. Recently modified content may be prioritised for queries where timeliness matters (pricing, features, comparisons).
Action: Update key pages at least quarterly. Reflect the update in your dateModified schema and sitemap lastmod.
6. Topical authority
The model evaluates how deeply your site covers a topic. A single blog post about "CRM software" is less authoritative than a site with 20 interlinked articles covering CRM features, comparisons, case studies, and guides.
Action: Build topic clusters — a pillar page plus supporting articles, all interlinked. This is exactly what the GEO strategy recommends.
7. Tables and lists
Tabular and list-based content is extracted by AI models far more reliably than prose paragraphs. Comparison tables, feature lists, and specification grids are among the most frequently cited content types.
Action: If you are comparing, listing, or summarising anything, put it in a table or list.
8. FAQ sections
FAQ content directly mirrors the question-answer format of AI conversations. Adding a FAQ section with 3-5 questions — especially with FAQPage JSON-LD — gives the model high-confidence Q&A pairs to cite.
Action: Add a FAQ section to every page that answers a "What" or "How" question. Mark it up with FAQPage structured data.
9. Internal link density
Internal links help the model understand your site's content graph. Pages that are well-connected to related content signal topical depth and make it easier for the model to verify information across your site.
Action: Link every article to 3-5 related pages on your site. Use descriptive anchor text that includes the target page's topic.
10. Meta description quality
During retrieval, the model often sees your meta description before it reads the full page. A clear, citation-ready description under 160 characters acts as a first-pass relevance filter.
Action: Write meta descriptions that directly answer the page's core question. Avoid filler words and marketing fluff.
11. Domain authority
While LLMs do not use a "domain authority" score directly, they inherit authority signals from the search indices they rely on for retrieval. Established domains with strong backlink profiles tend to be retrieved more often.
Action: This factor takes time. Focus on earning quality backlinks through authoritative content. Your existing SEO efforts compound here.
12. Content depth
Comprehensive, thorough coverage of a topic signals to the model that your page is a reliable source. Thin content with surface-level information is less likely to be cited than a detailed, well-researched article.
Action: Aim for substantive coverage that answers the main question and its follow-ups. This does not mean writing longer — it means writing more completely.
How LLM factors differ from Google factors
| Factor | Google weight | LLM weight |
|---|---|---|
| Backlinks | Very high | Low-medium (indirect via retrieval) |
| Page speed / Core Web Vitals | High | Negligible |
| Keyword density | Medium | Low |
| Structured data (JSON-LD) | Medium | Very high |
| Direct answer / extraction ease | Low | Very high |
| Tables and lists | Low-medium | High |
| Entity clarity | Low | High |
| Content freshness | Medium | High |
The key takeaway: Google rewards pages that users click on. LLMs reward pages that AI can extract clear facts from. The strategies overlap, but the priorities are different. For a full comparison, see GEO vs SEO.
Quick-start optimisation checklist
- Add JSON-LD (Article, FAQPage, Organization) to your top 10 pages
- Write a Direct Answer in the first 200 words of each page
- Clean up heading hierarchy — one H1, descriptive H2s, question-based H3s
- Standardise brand naming across all pages
- Convert comparisons and data into HTML tables
- Add FAQ sections with 3-5 real questions per page
- Interlink every article to 3-5 related pages
- Rewrite meta descriptions to be citation-ready (under 160 chars)
- Run a GEO Content Audit to validate your changes
- Track AI Share of Voice before and after optimisation
Frequently asked questions
Check your ranking factors now
Run a GEO Content Audit and see which of the 12 factors your pages already have — and which ones are missing.