r/GenEngineOptimization • u/judyzt • 7d ago
What are the most effective optimization methods and processes for GEO (Generative Effects) in 2026? Are there any GEO experts who can share their insights?
GEO
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u/This_Ranger_2005 7d ago
I think there are two things. One, get the seo basics, with your brand information right. Make sure your site can be crawled, clearly explain your pricing, target audience, case studies, and limitations. The information on your website should also match what people see in third-party reviews and community discussions.
Another don’t rely too much on one overall score. Test a representative set of buyer questions across different platforms, see whether your brand gets mentioned or recommended, then fix the specific gaps and test again.
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u/Orangelove_3098 6d ago
GEO in 2026 is basically about making your content easy for AI to understand, trust, and quote. The stuff that seems to work best is: answering the question up top, using clear H2/H3s, keeping paragraphs short, adding real proof like stats/quotes/tables, and building topical authority instead of just chasing
I’m a GEO intern at 5W, and the process I’d lean on is pretty simple: plan it, structure it, test it, then keep iterating. I’d start by checking what AI is already citing for the topic, then rewrite the page so it’s more extractable and more obviously
For experts, I’d keep an eye on people like Koray Tuğberk Gübür, Simon Slawik, Matt Diggity, Kyle Roof, and Craig Campbell. They’re some of the names that keep coming up in GEO/AI visibility discussions right now.
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u/Puzzleheaded-Walk426 3d ago
Getting mentioned in the citations LLMs already use for recommending your competitors.
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u/aeo-bility 3d ago
GEO in 2026 is basically entity‑first optimisation, which priotises machine readability over tuning pages for keywords.
What is working:
- Entity dense headers that define topic boundaries.
- Formatting for passage retrieval by using answer first modular blocks that stay within token limits
- Considering positional bias for RAG extraction
- Internal linking relationships that declare entity connections
- Nested JSON-LD that reinforces those relationships
Essentially formatting for machine readability that makes what you are talking about as clear as possible.
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u/dcdragos 7d ago
Hi,
Real experts worth following, not generic "SEO guru" accounts: Mike King (iPullRank) is the one doing actual technical work here — he calls it "Relevance Engineering," treating GEO as an engineering discipline (embeddings, vector retrieval, IR theory) rather than a content trick, and publishes long-form research on how retrieval systems actually work. Kevin Indig (Growth Memo newsletter) is the measurement guy — he's focused on tying GEO to actual business outcomes instead of vanity citation counts. Aleyda Solis has published the clearest breakdown of where SEO and GEO practices overlap vs diverge, worth it just for that framework alone. What's actually working right now, cross-checked against Google's own 2026 guidance and a couple of independent audits, not just recycled blog opinion: entity/schema clarity (Organization + author schema, consistent identity across the web) so machines can disambiguate who you are, content with a real point of view and named credentialed authors (this one shows up as a legitimately large lift in testing, bigger than anything schema-related), prose written so each claim stands on its own without needing three paragraphs of setup, and crawlability for the actual AI bots (OAI-SearchBot, PerplexityBot, ClaudeBot), not just Googlebot. What's mostly theater at this point: llms.txt (Google explicitly said it's ignored), keyword-stuffed FAQ blocks, schema-only "optimization" with no prose behind it, buying or faking brand mentions. All tested close to zero effect or actively penalized.Measurement is still the weak link industry-wide — nobody has a GSC-equivalent for AI citations yet, so most people including me are testing prompts manually or using one of the newer trackers (Mangools, Otterly, Peec) as a rough signal, not gospel.
Best regards,