Ranking in AI Search: Lessons from 30 experts on how to get cited by LLMs

AI search has rewritten the rules of visibility, and most SEO playbooks haven’t caught up. To understand what actually gets a brand cited by LLMs: ChatGPT, Perplexity, Gemini, and Google’s AI Overviews, I asked 30 experts who are actively testing, and winning, in AI Search.

Not opinions. Not theory. Real experiments.

Their insights were surprisingly aligned: AI engines rewards clarity, structure, specificity, and truth over polish or volume.

This article distills the strongest tactics from those 30 practitioners into a practical playbook for how to rank on AI search engines in 2026.

Pattern #1: Answer-first, structured, and legible

What they’re seeing?

David Hunt (Versys Media) rebuilt B2B solution pages into narrow topical hubs wuth: problem statement “when to use this,” and a short comparison block. Result: those pages started getting cited in LLM answers for precise “tools for X in Y industry” queries.

Danyon Togia (Expert SEO) gets AI mentions with clean formatting: upfront answers, correct heading hierarchy, FAQs, lists, and tables, plus traditional link building and strong reviews.

Why this works, because LLMs:

• Parse content as chunks and want self-contained answer capsules that match natural questions.
• Prefer passages that can be dropped into an answer without editing, short, factual, and aligned with the query intent.
• Map questions to entities + relations, not just keywords; consistent naming and clear entities boost retrievability.

Jon Kelly’s (Hyperlinks) analysis backs this up:

“One example is from a recent analysis of nearly two million sessions that showed 72 percent of ChatGPT citations come from short answer capsules placed right after an H2 question. These capsules are usually around a sentence long and contain no hyperlinks at all. It surprised me at first, but it makes sense. When the text is self-contained and not trying to send the user somewhere else, the LLM sees it as a complete answer. Adding proprietary data on top of that, even a single branded stat or benchmark, increases the chances of being cited even more.”

Quick wins

For your top 10–20 money pages:

1. Add an H2 that literally matches the question users would ask an LLM
   • H2: "Is <Product> the right tool for <Audience>?"
   • H2: "How much does <Service> cost in <City>?"
2. Place a one–two sentence “answer capsule” immediately after the H2: No fluff, no links, no hedging, just the best, clearest answer.
3. Follow with structured support: Bullet lists, numbered steps, short tables, FAQ schema.
4. Standardize terminology and entity names across your site: One clear product name, one category descriptor, repeated everywhere.

Pattern #2: Operational, “boringly specific” content beats polished marketing

What they’re seeing?

• Gregory Shein (Nomadic Soft / Corcava):
  • Pages showing step-by-step reasoning and transparent dev processes produced a 51% increase in AI-surfaced snippets in eight weeks.
  • Highly templated, SEO-perfect pages were blurred into competitors or ignored.

Why this works?

AI search is building a blueprint, not grading your prose.

Retrieval pipelines look for:

• Procedural knowledge: checklists, workflows, timelines, troubleshooting flows.
• Slots to fill: numbers, ranges, constraints, requirements.
• Distinctive edges: operational details competitors don’t expose.

Case studies and process content give the model reusable steps. Checklists and SOPs give it safe default behaviors to recommend.

Quick wins

1. Turn your internal SOPs into public playbooks: Onboarding checklists, implementation runbooks, troubleshooting documentation, internal QA steps.
2. Promote “deep FAQ” sections over generic hero copy: As Gary notes, buried troubleshooting sections are where long-tail AI queries actually land.
3. Make spec tables HTML, not PDFs: Stephen’s team saw PDFs get ignored until they converted specs into HTML tables with canonical model IDs.
4. Rewrite at least 5 marketing pages into “how we actually do this” walkthroughs: Think “behind the curtain” pages that no competitor is brave enough to publish.

Pattern #3: Evidence, numbers, and proprietary data become is the fastest way to get cited by LLMs

What they’re seeing?

• Meyr Aviv (iMoving): adding precise price corridors ($1,500–$3,500 for most interstate moves) caused a spike in citations from AI assistants that had never referenced them before.
• Mark Sanchez (Gator Rated): guides that combined specific local price trends with structured FAQs saw a 28% increase in impressions from AI-driven search.

Why this works?

LLMs are risk-averse by design:

• They prefer statements they can reuse without hallucinating, that means clear numbers, ranges, definitions, and decision rules.
• They cross-check across sources; consistent numbers and phrasing across your site, docs, and third-party mentions strengthen your “material truth” in the model’s graph.

Erwin Gutenkust’s (CEO at Neolithic Materials) experience in the interior design industry supports this:

“Long-form case studies showcasing reclaimed limestone in nontraditional settings, like pairing 200-year-old French pavers with contemporary steelwork, ranked dramatically higher in LLM responses than polished commercial copy. AI systems rewarded originality, specificity, and provenance. Conversely, generic feature lists underperformed regardless of keyword density.”

Quick wins

1. Add at least one proprietary stat or range to every major page: Benchmarks, average ROI, typical timelines, cost corridors, defect rates, etc.
2. Rewrite fluffy benefit claims into testable statements: From “we’re fast” to “average turnaround is 3.2 days across 417 projects.”
3. Create a “Data & Benchmarks” hub: One page with all your key numbers; make other pages link back and reuse these numbers verbatim.
4. Turn internal analyses into public mini-studies: Follow Bharath’s lead: publish methodology + visualizations and you become a reference, not just a commentator.

Pattern #4: Entities, authority, and third-party proof drive inclusion

What they’re seeing?

• Danyon Togia (Expert SEO): emphasizes reviews and third-party sites as crucial inputs; AI systems pull a lot from off-site reputation.
• Anusha Ray (MarketEngine) and Corina Tham (CheapForexVPS) both highlight the value of spreading consistent brand messaging across LinkedIn, Reddit, Quora, Medium, etc. LLMs ingest the full ecosystem, not just your site.

Quick wins

1. Clean up entity schema across your site: Organization, Product, FAQ, Article, Service, Person (for key leaders). Link them.
2. Standardize your “entity sentence: One sentence that describes who you are, what you do, and for whom, reused on site, LinkedIn, profiles, and media bios.
3. Run a review & PR sprint: Target 2–3 credible third-party platforms + 1–2 relevant podcasts or industry outlets; aim for cited quotes and detailed write-ups, not generic mentions.
4. Audit branded queries inside AI systems: Ask ChatGPT, Perplexity, Gemini: “Who is <brand>?”, “Best tools for <category>.” Note which entities they mention, and where they’re getting that info.

Pattern #5: Freshness, experimentation, and measurement are non-negotiable

What they’re seeing?

• Ronak Kadhi (Bundled.design):
  • Pages updated every 4–6 weeks show up more in GPT and Perplexity.
  • LLMs prefer short summaries, problem→solution blocks, and micro examples over walls of text.
• Anusha Ray (MarketEngine): Refresh key pages every 60–90 days with updated stats, examples, and FAQs; content velocity and freshness correlate with AI Overview improvements.
• Corina Tham (CheapForexVPS) stresses continuous experimentation with GEO, AEO, and long-tail variants, LLMs reward brands that keep feeding high-signal, high-relevance content.
• Dennis Shirshikov (Growth Limit) and Mădălina “Mada” Seghete (Upside) both push “answer clusters” and extraction-friendly content, then evaluate what gets cited to refine structure.
• Samuel (Adegideon) notes that GEO works best when paired with real location-specific value, AEO works when your structure is answer-first, and AI SEO works when AI is the assistant, not the author.

Quick wins

1. Pick 50–100 target queries and start an “AI panel log.”: Weekly screenshots / HTML captures from Google, ChatGPT, Perplexity, Claude. Track if you’re cited, where, and alongside whom.
2. Set a “freshness SLA” for top pages: Every 60–90 days: add new stats, examples, FAQs, and micro case studies.
3. Treat AI experiments like CRO tests: Change one structural element at a time (e.g., answer capsule, table, FAQ order) and monitor citation frequency.
4. Build a simple internal KPI set for GEO: Chunk retrieval frequency, AI citation count, AI share of voice, branded search lift.