Be the Answer: GEO for the Train + Fetch Era of LLMs
Generative Engine Optimization for LLM training and real-time fetch
We tuned a decade of marketing for Google’s crawler. Discovery now runs through generative engines that don’t rank; they remember, fetch, and recommend. The goal isn’t #1 on a SERP (Search Engine Results Page). It’s to be the source an LLM (Large Language Model) reaches for.
That’s GEO. Generative Engine Optimization: making your product the default answer in a world where models both train on your content and fetch it on demand.
GEO in Two Pipelines
1) GEO-Train: Optimize for what models learn
Large models periodically ingest big snapshots of the public web. They compress patterns: entities, relationships, and “who solves what for whom.”
-
Consistent label used verbatim everywhere:
“[Your SaaS] is a [category] for [ICP] that solves [use case].”
-
Context edges: interlink use cases, alternatives, integrations, problems, personas.
-
Answer-first pages (not 2,000 words of fluff): “What is…,” “Best tools for…,” “[Brand] alternatives,” and case studies.
-
High-signal about: who you serve, proof, pricing model, stack, clear category.
-
Branded anchors you want repeated: “[Brand] is a private video hosting SaaS for course creators.”
|
Think: you’re training a label the model will remember and reuse. |
2) GEO-Fetch: Optimize for what models cite right now
When a query is specific or time-sensitive, assistants ground answers with fresh sources (RAG-style): search → retrieve passages → rank → compose → cite.
-
Fetchable structure: short sections, scannable bullets, key facts near the top.
-
Freshness: “last updated” stamps, stable URLs for pricing/limits/changelogs.
-
Semantic scaffolding: SoftwareApplication, Organization, Product, FAQPage, HowTo schema; XML sitemaps with lastmod; RSS/Atom feeds.
-
Coverage off-site: Medium/Substack, Product Hunt, relevant Reddit/Quora/Stack Overflow threads, digital PR. Each is a trust vector models can pull from.
|
Think: you’re maximizing recall and verification at answer time. |
What GEO Means for SEO (and Why Keywords Fade)
Old SEO: pick a keyword, chase volume, sprinkle synonyms.
GEO: models resolve intent across entities and evidence, then either recall trained representations (GEO-Train) or fetch current proof (GEO-Fetch).
-
Entities > keywords: clear category + ICP + use case beats phrase stuffing.
-
Answers > articles: the page that resolves the job wins.
-
Signals > density: interlinking, schema, corroboration, and freshness matter most.
-
Volume ≠ demand: prompt intent is fragmented; “keyword volume” is a weak proxy.
|
Keywords aren't dead. They're just inputs, not the strategy. |
The Practical GEO Playbook: How To Rank for LLMs
-
Declare the label once and repeat it everywhere (Home, About, Features, blog intros, docs).
-
Build context clusters: use cases, alternatives, integrations, problems, personas.
-
Publish answer-first pages for every high-intent question.
-
Harden the about page with facts models like to quote.
-
Annotate with schema; maintain sitemaps/feeds; keep slugs stable.
-
Refresh fetch targets (pricing, limits, integrations, changelog) with timestamps.
-
Syndicate to trusted vectors beyond your site; earn branded anchors.
-
Write contrasts that models can reuse: “Unlike tools for [X], [Your SaaS] does [Y] in a privacy-first way.”
-
Measure GEO: AI citations/mentions, referral from AI surfaces, freshness of fetchable pages, schema coverage, branded-anchor share.
-
Tight loops: update core facts often; keep answers short and link to deeper docs.
Google’s Ad Model vs. LLM Answers
Today’s monetization: Google sells high-value ad real estate around a list of links; revenue depends on clicks.
-
Most revenue depends on clicks to advertisers’ sites. More links → more ad slots → more chances to click.
-
Inline answers collapse the page: fewer links, fewer ad slots, fewer clicks—even inside Google’s own Gemini/AI Overviews.
-
Expect experiments: ads inside answers, shoppable modules, affiliate/commerce rev-share, and paid prioritization in certain verticals.
Your move: Become the cited source in those answers.
-
Make commercial facts easy to quote.
-
Push brandable phrasing so you're named and not just described.
- Build direct channels (newsletter, community, product updates) for demand capture without the click.
Quick GEO Checklist (Train + Fetch): Repeatable Processes for Crafting GEO-Friendly Content
✅One-sentence positioning repeated verbatim across the site
✅Interlinked clusters: use cases, alternatives, integrations, problems, personas
✅Answer-first pages for every high-intent task
✅High-signal about page with proof and labels
✅Schema (SoftwareApplication, Organization, Product, FAQPage, HowTo), sitemaps with lastmod, RSS/Atom
✅Stable, fetchable URLs for pricing/limits/integrations; visible “last updated”
✅Off-site corroboration (PR, forums, reviews, Product Hunt, Medium/Substack)
✅Branded anchors earned and reused
✅Track AI citations, fetchable-page freshness, schema coverage
The Shift
Classic SEO optimized for a crawler. GEO optimizes for memory and fetch.
Train models to remember your label, and structure your site so they fetch and cite you when it matters.
Do that, and you won’t just appear in answers. You’ll be the answer.
About the Author
Joel is CTO at Agility. His first job, though, is as a father to 2 amazing humans.
Joining Agility in 2005, he has over 20 years of experience in software development and product management. He embraced cloud technology as a groundbreaking concept over a decade ago, and he continues to help customers adopt new technology with hybrid frameworks and the Jamstack. He holds a degree from The University of Guelph in English and Computer Science. He's led Agility CMS to many awards and accolades during his tenure such as being named the Best Cloud CMS by CMS Critic, as a leader on G2.com for Headless CMS, and a leader in Customer Experience on Gartner Peer Insights.
As CTO, Joel oversees the Product team, as well as working closely with the Growth and Customer Success teams. When he's not kicking butt with Agility, Joel coaches high-school football and directs musical theatre. Learn more about Joel HERE.
