Get Cited by AI: A Practical, Evidence‑Based Playbook (2025)

AI assistants cite pages that answer cleanly, verify claims, and stay fresh. Across millions of citations and multiple platforms, a simple pattern holds: clear “answer blocks,” credible sources, and real updates beat tactics and guesswork. Use the playbook below to ship changes this week.

TL;DR

Focus on clear, quotable “answer blocks” with dates, scope, and sources.
Keep key info in HTML text; match structured data to visible content.
Freshness matters on average (~25.7%), but Google AI Overviews is less sensitive—don’t fake “last updated.”
Platform patterns differ (ChatGPT → Wikipedia; AI Overviews → community/video). Plan and measure per engine.
Start here: pick your top 5 pages, add answer blocks + Q&A, then track citations per platform.

How AI systems actually pick sources

Google (AI Overviews & AI Mode): Google says there are no extra tags or special schema for inclusion in AI features; standard SEO best practices apply. Keep important content in text, ensure internal links, and make sure structured data matches visible text. Google notes AI features may use “query fan-out” to gather diverse supporting links, and AI traffic is counted under the “Web” type in Search Console.
Observed platform differences: Profound’s 30M-citation analysis (Aug 2024–Jun 2025) finds ChatGPT leans heavily on Wikipedia, while Google AI Overviews frequently cites community/video sources (e.g., Reddit/YouTube); patterns vary by engine. Plan per platform.
Freshness preference (on average): A 17-million-citation Ahrefs study shows AI assistants cite content that’s ~25.7% “fresher” than organic Google results. Among assistants, ChatGPT showed the strongest freshness preference; Google AI Overviews was closest to classic SERPs (least freshness-sensitive).

Build answer blocks that travel

1) Lead with a verifiable claim (and source it)

Make the first paragraph a definitive, source-anchored statement that can stand alone as a quote.

Example (strong):
“Compared with organic SERPs, major AI assistants cite content that is ~25.7% fresher on average (analysis of 17M citations, 2025).”

Why this works: Assistants extract self-contained ideas; Q&A-style and clearly labeled sections are consistently easier for automated answer systems to lift. Microsoft’s question-answering best practices emphasize clean Q–A pairs and clear headings.

2) Use hierarchical, self-contained “answer blocks”

One idea per H2/H3.
A 2–5 sentence summary paragraph that can be lifted alone.
Add a compact list or table for key variables (metric, scope, date, source).

This aligns with scannable structure (headings/lists) and FAQ-style extraction guidance.

3) Include specific, checkable data points

Dates, sample sizes, scope (e.g., “17M citations across 7 platforms”).
Platform differences (e.g., ChatGPT→Wikipedia; AI Overviews→community/video sources).

Tip: Don’t dump numbers—state the date, scope, and source so a single paragraph is quotable without ambiguity.

Templates you can reuse

Answer block template

Summary: 2–5 sentences with a clear claim, timeframe, and implication.
Details: 3–5 bullets or a tiny table with metric, scope, date, and source.
Link to the primary source.

Q&A snippet template

Q: Does Google require special schema for AI features?
A: No. Google states there’s no special schema; follow core SEO, keep key info in text, and ensure structured data matches visible content. Source: Search Central.

Do / Don’t

Do: “In 2025, assistants cite content ~25.7% fresher (17M citations; Ahrefs).”
Don’t: “AI prefers fresh content.” (No date, no source, no scope.)

Writing style that improves selection

Be definitive, not speculative. Tie non-obvious claims to a primary doc or a large-scale study.
Use active voice and plain language for snippable, quotable blocks.
Add context: define terms, bound claims (timeframe/platform), and explain relevance.

Reality check: Opinions alone rarely drive citations. Favor verifiable context and test it in your niche.

Technical checklist

No special AI markup for Google features. Follow core SEO: crawlability, internal links, keep key info in HTML text, and ensure structured data matches visible content.
Structure for extraction. Align title/H1/meta, use descriptive H2/H3, and include Q&A blocks, concise lists, and small tables where they clarify the answer.
Freshness & discovery. Update substantively (avoid cosmetic date bumps). For Bing-powered surfaces, add IndexNow alongside sitemaps (lastmod); submissions don’t guarantee immediate indexing.
Internal linking. Surface cornerstone pages; keep crawl paths short and logical.
Performance & UX. Fast, ad-light pages reduce abandonment and help assistants quote cleanly.

Building authority signals (what helps in practice)

People-first, reliable content (E-E-A-T-aligned). Helpful, reliable content wins; author transparency and primary sourcing help.
Platform-fit distribution:
- For ChatGPT, publish clean, factual explainers with clear summaries (Wikipedia-like).
- For Google AI Overviews, community/video sources (e.g., Reddit/YouTube) appear often; add practical, experience-based context and quality video where relevant.

Note: There’s overlap between AI Overviews citations and high-ranking pages, but no proven causality. Treat snippets as helpful—not a prerequisite.

Formats that consistently perform

How-to guides & tutorials with steps and a short “what/why/when” intro—easy to quote as complete ideas.
Original research & industry reports (state methodology, timeframe, limitations). Strong citation magnets across platforms.
Statistical compilations with sources and dates (avoid orphan stats without provenance).
Comparisons with compact tables (criteria, pros/cons, when to choose X vs. Y). Our meta-study of AEO/GEO tool comparison pages found that 5 of 9 articles were vendor-authored—proof that SEO companies themselves rely on the comparison format to drive visibility and citations.

Platform differences you should plan for

Platform	Observed citation patterns	Practical implication
ChatGPT	Leans heavily on Wikipedia among top sources.	Publish definitive, well-sourced explainers with clear summaries.
Google AI Overviews	Frequently cites community/video sources (e.g., Reddit, YouTube) along with pro sites.	Add practical, experience-based context; use quality video where appropriate; maintain standard SEO quality.
Perplexity	Strong presence of community and mixed sources; patterns differ from ChatGPT.	Provide concise, verifiable answers and engage where communities discuss your topic.

Large cross-engine studies show meaningful differences—optimize and measure per platform.

Measuring “AI citation” success (and avoiding vanity metrics)

Google Search Console: AI features (AI Overviews / AI Mode) are rolled into overall “Web” performance; there’s no separate AI Overviews report. Expect some zero-click behavior.
Use a reputable monitoring tool to see which assistants cite you.

Instrument your own tests: track per-URL AI mentions with your monitoring tool, annotate updates, and compare against control pages.

Try this in 30 minutes

Pick one high-potential URL.
Add a 4-sentence answer block with date, scope, and source.
Add one Q&A snippet.
Link related internal pages; make the crawl path obvious.
If relevant, submit via IndexNow (Bing surfaces) and ensure sitemaps lastmod is accurate.
Track citations over the next 7–14 days.

Practical checklist (copy/paste)

Define the claim in the first 2–3 sentences and cite it.
One idea per section (H2/H3), each with a stand-alone summary paragraph.
Add Q&A blocks, short lists/steps, and small tables where they improve clarity.
Keep core info in HTML text; avoid image-only/PDF-only for essentials.
Update substantive pages periodically; don’t “date-bump” without changes. For Bing-powered surfaces, add IndexNow alongside sitemaps (lastmod).
Disclose methodology (sample size, timeframe) for any stats you publish.
Measure citations per platform with your monitoring tool and iterate.

Myths vs. data (what to stop doing)

“Just add schema for AI.” → Google states no special schema; use structured data for clarity/rich results and ensure it matches visible text.
“Winning featured snippets guarantees AI citations.” → Overlap exists with high-ranking pages, but causality isn’t proven. Measure in your niche.
“Updating dates alone boosts AI citations.” → Assistants skew fresher overall, but Google AI Overviews is comparatively less freshness-sensitive.
“Hot takes get you cited.” → Opinions alone rarely move the needle; verifiable, bounded answers do.
“All engines behave the same.” → They don’t. Optimize and measure per platform.

When advice feels generic, verify it first with a reputable monitoring tool before you scale it.

Example: citation-friendly block you can reuse

What changed in 2025?
Large-scale analyses show AI assistants cite newer content than traditional SERPs (~25.7% fresher on average; 17M citations), but Google AI Overviews remains closest to organic rankings in freshness sensitivity. Implication: publish genuinely updated, high-signal material; don’t rely on date-only refreshes.

Extra: how ChatGPT browses (to reverse-engineer eligibility)

Independent testing notes that ChatGPT (when browsing) often issues multiple targeted queries, applies recency filters, and favors credible/official sources—so ensure your page can be found across several precise queries and that your author/source signals are explicit.

Measure and iterate with Bourd

AI search evolves fast. Today’s best practices can shift within months. The durable edge is a feedback loop: monitor citations, ship structured updates, and validate what works. Bourd helps you track LLM mentions across major assistants, run data‑driven content experiments, and see which changes move the needle by page and platform.

Ready to measure your impact? Sign up at Bourd

Current as of 9 Oct 2025.