I ran a small GEO experiment to see what actually makes content show up in ChatGPT and Perplexity answers

I ran a small GEO experiment on our SaaS blog recently because something felt off.

We rank decently in Google for several mid-tail keywords in our niche, but when prospects told us they were researching tools using ChatGPT and Perplexity, our brand almost never appeared in the answers. So I decided to test something simple: What happens if you write content specifically for LLM citation, not traditional SEO?

The experiment - Timeline: ~6 weeks. We published 18 articles designed primarily for retrieval and citation rather than keyword density or narrative blog style.

Before starting, I ran about 25 prompt variations in ChatGPT and Perplexity for queries relevant to our product category. Result: 0 citations from our domain.

Hypothesis - After reviewing a lot of AI answers, my working theory was that LLMs tend to prefer content that is: structured around explicit questions, entity-rich (brands, tools, concepts clearly referenced), concise answers early in the page, supported with citations or schema. Basically: closer to a reference page than a storytelling blog post.  

Content structure we used - Each article followed the same pattern: H1 = core question, short definition paragraph (40-60 words), several H2 questions that map to likely sub-prompts, direct answer block under each heading, references to relevant entities (tools, companies, concepts), FAQ schema at the bottom. We also interlinked entity relationships aggressively.  

Traditional SEO intros (the 3-4 paragraph storytelling lead-in) were removed entirely.  

Production workflow - Writing 18 of these manually would have taken forever, so part of the test used automation tools. For the actual content pipeline I experimented with SurferSEO briefs, Jasper drafts, and one automated workflow with this SEO tool that handled keyword discovery, writing, internal linking, and publishing.

Results after ~6 weeks - Baseline before experiment: 0 AI citations detected.

After publishing the 18 articles: 7 citations across ChatGPT and Perplexity, ~11 appearances in AI answers during manual prompt testing, Google rankings changed very little during the test window.

Which was interesting because the content clearly affected AI retrieval faster than classic SEO movement.

What seemed to matter most - Explicit question headings (mirroring common prompts), short definition paragraphs near the top, entity references to known tools/brands, clean schema + structured FAQ blocks. When those elements were missing, the page rarely surfaced.  

What didn't seem to matter much - keyword density, long narrative intros, heavy "SEO copywriting" style content. In fact, some of the pages with the most classic SEO formatting were the least likely to get cited.

Practical takeaway - If you're trying to increase LLM citations, writing style probably needs to shift from blog storytelling to something closer to a reference document that answers questions quickly.

  Think: Question → definition → entity context → structured answer blocks.  

Curious if anyone else here has run structured experiments around this. Are you seeing similar patterns with entity density and question formatting driving citations?