Column
GEO · Generative Engine Optimization
When search stops giving links and starts giving answers
Search is shifting from “here are ten links” to “here is the answer.” Visibility is no longer decided by click-through rate, but by the probability of being cited by an AI engine.
This column systematically unpacks Generative Engine Optimization (GEO): how it diverges from classic SEO, how AI engines choose their sources, how to structure content to be cited, and a practical checklist you can apply today.
The column is ongoing — the complete guide is published, with more installments on the way.
Contents
Best read in orderGEO in 2026: Evidence, Limits, and a Practical Workflow
An evidence-led guide to generative engine optimization in 2026: what Google, OpenAI, Perplexity, Pew, Bain, and the original GEO paper actually support.
How AI Search Retrieves and Cites Sources: A Testable GEO Model
A practical 2026 guide to how Google, Perplexity, and ChatGPT retrieve and cite sources, separating official facts from inference and showing how to test GEO.
Structured Content for GEO: A Reader-First Playbook
A practical guide to clearer technical articles: lead with useful answers, use honest evidence, add valid schema, build internal links, and test AI visibility.
Off-Site Trust for GEO: Identity, Evidence, and Ethical Distribution
A practical 2026 guide to off-site GEO trust: author identity, third-party evidence, ethical community distribution, and repeatable citation measurement.
GEO Blog Rebuild Case Study: Running the Five-Layer Model on Real Data
A real-data GEO case study using Search Console and Lighthouse to separate noisy impressions from useful demand, protect a domain move, and plan a Hugo rebuild.
GEO Measurement in 2026: A Reproducible Citation and Referral Protocol
Measure GEO without proxy myths: combine Google generative AI impressions, repeated citation audits, referral analytics, and conversions in one clear protocol.