Column

GEO · Generative Engine Optimization

When search stops giving links and starts giving answers

6 essays · 54 min total

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

  1. GEO: The Complete Guide to Generative Engine Optimization (When Search Stops Giving Links and Starts Giving Answers)

    When 68% of Google searches no longer produce a click and AI hands the answer straight to the user, the "rankings" that classic SEO fights for are quietly losing value. GEO (Generative Engine Optimization) fights for something else: getting the AI to understand, trust, and cite you when it writes the answer. A pillar-length guide from first principles to methodology to a real case study on my own blog — and the opening chapter of the GEO series.

    · 15 min

  2. GEO Mechanics: How AI Retrieves, Re-ranks, and Cites You

    To get cited by AI, first understand how it picks. This chapter takes the RAG pipeline apart to the component level: query fan-out, hybrid retrieval, vector semantics, multi-stage reranking, and citations pre-embedded before generation. The one core takeaway — the retrieval unit is the passage, not the page. Optimize the chunk. Chapter 2 of the GEO series.

    · 10 min

  3. GEO Structured Tactics: Writing "Worth Citing" Into Every Paragraph (Answer-First, Schema, llms.txt)

    Principles done — this chapter is all hands-on: how to write Answer-First paragraphs, turn headings into questions, whether FAQPage/HowTo schema still matters after Google retired the rich results, the right way to do llms.txt and tldr, and how to weave internal links into a topic cluster. With code and before/after. Chapter 3 of the GEO series.

    · 8 min

  4. GEO Trust & Endorsement: Why Reddit and Wikipedia Make Up Half of AI Citations

    Your technical base and structure are right — so why still no AI citations? Because the final gate is trust, and most trust comes from off-site. This chapter covers operationalizing E-E-A-T, building entity consistency, why Reddit + Wikipedia are 66% of AI citations, and how a personal blog builds off-site endorsement pragmatically. Chapter 4 of the GEO series.

    · 7 min

  5. GEO Blog Rebuild Case Study: Running the Five-Layer Model on Real Data

    Four chapters of method — now real data. I dug through cubxxw.com's Google Search Console and PageSpeed Insights and diagnosed it layer by layer with the five-layer model: why 878K impressions produced only 852 clicks, which queries are noise and which are gold, how to protect a domain migration, and a priority-ranked rebuild checklist. Chapter 5 of the GEO series.

    · 7 min

  6. GEO Measurement & Tools: How to Know If AI Actually Cites You (with a DIY Monitor)

    Classic "rank + click" fails in the GEO era because most value happens where the user never visits you. This final chapter gives you a workable measurement system: prompt testing, AI referral traffic, GSC cross-check, dedicated tools (Profound/Peec), and a low-cost DIY monitor built on this repo's own scripts. Chapter 6 (finale) of the GEO series.

    · 7 min