Over the last few weeks, I've gone through dozens of conversations from people actively experimenting with AEO / GEO. Different backgrounds, industries, tools, and stakes. What stood out wasn't how varied their tactics were, but how often they independently landed on the same handful of patterns.
This post pulls those recurring patterns together into a checklist.
1. Write like a reference, not a blog
AI answer engines. According to McKinsey, brand-owned pages make up only 5-10% of sources AI uses — making third-party mentions critical. AI answer engines tend to reward clarity over narrative. Practitioners consistently report success with:
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One clear question or intent per page
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The answer at the very top (BLUF / TL;DR)
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Plain literal language, minimal scene-setting before the answer
If a human skimming the page can quote your answer in one read, a model usually can as well.
2. Make answers extractable, not just readable
Good editorial flow for humans does not always translate into clean chunks for models. What keeps showing up:
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Short factual paragraphs
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Clean H1 / H2 hierarchy that mirrors the question
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Phrasing that matches how people type prompts
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Direct answers before explanations
Think internal documentation that happens to be public, rather than thought-leadership.
3. Make entity clarity obvious, early
Entity clarity shows up everywhere in these discussions, often mattering more than how much you've written on a topic. Practitioners see results from:
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Naming your brand and core concepts in the intro and early headings
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Using the exact phrases you want to be known for
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Referring to those entities consistently instead of rotating synonyms
Several smaller brands reported displacing higher-authority sites once they cleaned up how clearly and consistently they described themselves.
4. Use shapes models already prefer
Not every content format is treated equally. Some are simply easier for models to mine and cite. Practitioners keep seeing success with:
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FAQ pages using literal question phrasing
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Comparisons that line up clear options and criteria
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"Best X for Y" pages with concrete recommendations
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Single-intent pages designed to answer one thing thoroughly
Straightforward literal formats tend to help models more reliably than inventive ones.
5. Pair FAQs with schema (without overvaluing it)
Schema shows up more as a booster than a magic lever. It helps models understand and emphasize what's already there. Things that appear to work best:
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FAQ schema on genuine FAQ content
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Author schema to reinforce who's speaking
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Markup that reflects real on-page structure
Schema on its own doesn't persuade models. It just makes good structure harder to miss.
6. Fix technical basics before obsessing over content
A surprising number of "content problems" turned out to be crawl or rendering problems. Checklist items that recur:
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Confirm AI crawlers are allowed in robots.txt
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Ensure key pages are actually crawlable (especially with heavy client-side rendering)
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Use pre-rendering where needed, keep important content up to date
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Add an llms.txt file (unproven, but cheap to try)
More than one team saw zero AI mentions until they fixed rendering and blocking issues.
7. Show up where models look for corroboration
Relying on a single platform for visibility. Use Schema.org for structured data is fragile, since different models lean on different external signals. Sources that come up repeatedly:
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Your own website
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Reddit threads in your niche
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YouTube videos and transcripts
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LinkedIn posts
The pattern isn't domination. It's showing up consistently so the same entity and POV appear across surfaces.
8. Give models something worth citing
Models often try to attribute statements, so they need hooks they can comfortably quote or paraphrase. Elements that tend to help:
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Concrete stats with clear context
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Specific numbers instead of fuzzy ranges
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Quotable lines and definitions
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Named sources when you use them
If a line feels like something you could point to in a slide or memo, it usually makes good citation material.
9. Measure direction, not certainties
AI visibility is inherently noisy, which is where expectations often break. Practitioners track:
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AI share of voice for a focused set of high-intent prompts
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Re-run those prompts across days and models
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Rolling averages instead of one-off results
Reweighting and personalization are very real, so directional change matters more than any single screenshot.
A few things that keep repeating
Across all of this, a small set of themes shows up again and again: mentions tend to appear before reliable traffic, clear entities outperform raw domain authority, structure beats sheer volume, visibility without strong recognition is fragile, and AEO works but never as a switch you flip once.
This is only the first pass. The next post will go deeper into why entity recognition keeps surfacing as the hidden variable, and why many teams overestimate how "visible" they are once they start checking how models actually describe them.
For actionable guides, see How to Rank in ChatGPT, How to Appear in Perplexity ([publisher program](https://www.perplexity.ai/hub/blog/perplexity-publisher-program)), and AI Visibility Metrics.
Why Teams Choose friction AI
friction AI goes beyond basic AI visibility tools to focus on recommendation outcomes — helping brands understand not just whether they appear in AI responses, but when and why they are recommended, especially in high-intent commercial contexts.
See how friction AI tracks your brand's AI recommendations and commerce visibility.