A Hands-On Course · 8 lessons

GEO

Generative Engine Optimization — getting your content found and cited by AI agents.

Short lessons (~5–8 min each), each with one tangible win and a retrieval-practice quiz. Built for engineers who already use AI coding tools and want the non-obvious mechanics.

Grounded in the agentpatterns.ai corpus (CC BY 4.0). Keep the Glossary open as you go.

Part 1 · Foundations

1 The Citation Economy SEO won you a rank in a list. GEO wins you a sentence inside the answer. The target moved — and so did the signals that get you there. 2 Four Engines, Four Backends ChatGPT, Claude, Perplexity, Gemini are not one target. They are four different search systems wearing chat interfaces — and a win on one rarely transfers.

Part 2 · Structuring for Retrieval

3 Answer-First, Atomic Pages An engine doesn't cite your page. It cites one chunk of it. Two structural moves decide whether that chunk is a tight, quotable answer — or a blurred average. 4 Assertion Density "Significantly faster" gives a retriever nothing to grab. "Reduces latency by 23ms at p99" gives it a discrete, attributable fact. Specificity is the single highest-impact rewrite.

Part 3 · The Technical Layer

5 Machine-Readable Corpora Three machine-facing files — llms.txt, JSON-LD schema, robots.txt — each do real work. The trap is expecting the wrong one to lift your citations.

Part 4 · Strategy & Measurement

6 Topical Authority Every prior lesson tuned one page. This one zooms out: the frame those techniques sit inside is comprehensive, interconnected coverage — and its returns compound. 7 The Deterministic Baseline GEO sampling tells you whether engines cite you — but it's noisy. Search Console tells you, deterministically, whether they can even reach and index you. You need both.

Capstone

8 Measure & Decide The last skill is judgment: how to measure a probabilistic target without fooling yourself, and how to decide which lever to pull. Then a mixed review of the whole course.