On-site tactics for AI search: what actually helps you get quoted and cited

If you're new to the bigger picture, start with What is AI search?. If you want the citation mechanics, read How AI citations work. This guide is the on-site layer: how to write and structure pages so you're easier to quote.

Do "answer-style paragraphs" increase chances of being quoted?

Usually, yes. Not because AI systems "prefer a style," but because they need something they can lift cleanly.

An answer-style paragraph is a short, direct response that stands on its own. It avoids long setup. It defines terms. It includes the key constraint or nuance. And it doesn't rely on context from five paragraphs above.

A good pattern is:

• one sentence that answers the question
• one sentence that adds the main nuance or condition
• optional: one example if it clarifies

If you do this well, you're not "writing for bots." You're just being clear.

Should I write "definition blocks" near the top of pages?

Yes, most of the time.

A definition block near the top helps in two ways:

• Humans get oriented instantly.
• Retrieval systems can quickly identify what the page is about and what chunk to quote.

Keep it short (2–3 sentences). Don't label it "Answer Block." Just style it like a callout: "Definition" or "In simple terms."

If your page is a comparison, the definition can be: "X vs Y in one sentence" plus "who should choose what."

What page structure is most "extractable" for LLMs?

The structure that works best is boring on purpose:

• clear H1 that matches intent
• short intro
• TL;DR
• definition / direct answer near the top
• H2s that mirror real questions
• short sections (5–10 lines)
• one idea per section
• a small FAQ when the topic has predictable follow-ups

Extractability improves when a page is easy to skim. If a section title describes exactly what the section answers, you've already done half the job.

Related: Extractability, Content chunking.

Does FAQ schema still matter and does it help AI citations?

FAQ schema can help machines interpret your Q&A, but it doesn't guarantee citations.

Use FAQPage schema when:

• you genuinely have a Q&A section that answers common follow-ups
• the questions are real and specific (not marketing fluff)
• the answers are concise and accurate

Don't use it when:

• you're stuffing questions that aren't actually asked
• the page doesn't have a real FAQ section

Also note: even when FAQ rich results change over time, the content itself still helps because it's inherently extractable.

What structured data should I use for AI search?

Use the structured data that accurately describes the page, not whatever sounds trendy.

Start with:

• Organization (site-wide)
• WebSite + SearchAction (if you have on-site search)
• Article / BlogPosting for articles
• FAQPage for real FAQs
• Product for product pages (with truthful pricing/availability)
• SoftwareApplication when appropriate
• BreadcrumbList for navigation clarity

Implement in JSON-LD and keep it consistent.

Structured data helps reduce ambiguity. That's the real win.

Does llms.txt matter? What is it?

llms.txt is a proposed convention where you publish a machine-friendly page that points AI agents to your most important content.

Treat it like a "map," not a ranking lever.

• If agents or tools look for it, you've made their job easier.
• If they don't, it won't hurt you.

If you implement it, keep it simple:

• one short summary
• a curated list of key URLs (docs, integrations, pricing, flagship guides)

And keep it updated.

(Access layer matters too: see AI crawlers, indexing, and access.)

Should I create dedicated comparison pages ("X vs Y") for AI search?

Yes, if the comparison is real demand and you can be honest.

Comparison pages are citeable because:

• the intent is specific
• the structure is naturally chunkable (differences, pros/cons, who it's for)
• the user often wants a decisive answer

Also: don't create 50 thin comparisons. Create the few that match the prompts your audience actually asks.

Related: Comparison pages.

Content chunking: how to make pages easier to cite

Chunking is the art of making your page divisible into citeable parts.

Good chunks:

• have a clear H2/H3
• answer one question
• don't require context from the rest of the page
• include a constraint or nuance so they don't mislead when quoted

If you want citations to land on deep pages (not your homepage), chunking and internal linking are core levers. See AI citations and URL citation depth.

FAQ

Recommended page template (copy/paste)

Use this structure for any high-value page you want cited: