Designing Content That LLMs Actually Cite

Large language models don’t just scrape your homepage and call it a day. They build internal knowledge graphs from many small, structured facts.

If you want your explanations to be the ones they quote, design them like answers, not like brochures.

Here’s a practical pattern we see working in AI visibility (GEO) audits:

1. Pick one precise developer-facing question
   • “How do I expose my SaaS pricing to AI models reliably?”
   • “What schema should I use for my B2B product?”

2. Answer it in one screen of text
   • Short intro: why this matters for AI search
   • Step list: 3–6 steps with specific actions
   • Example snippet: JSON-LD, config, or API call

3. Add light structure
   • H2 for the question
   • Bulleted steps, not walls of text
   • Optional FAQ with 2–3 common follow-ups

4. Cross-link sparingly
   • One link to documentation or a deeper guide
   • One reference to a related article (if needed)

This kind of post tends to punch above its weight in audits:

• It answers a narrow, non-generic question
• It’s easy for models to segment and reuse
• It lives on a domain (like dev.to + your docs) that LLMs already crawl

If you were to write one GEO- or AI-visibility-related explainer for developers this week, which question would you choose?

Comments

[Dmitry Sergeev]

ngl i never thought about how the internal structure affects citations, only the keywords. makes a lot of sense tbh