When should you use GEO?

When you are writing a strategy deck for leadership. When you specifically mean ChatGPT/Perplexity/Gemini presence and not SERP rankings. When you want to separate "classic SEO" budget from "LLM visibility" budget.

Glossary

GEO — Generative Engine Optimization

GEO (Generative Engine Optimization) is the practice of making your brand correctly mentioned and cited in answers from ChatGPT, Gemini, Perplexity and other LLMs.

GEO is SEO for generated answers, not for blue-link rankings.
It overlaps with AEO and LLM SEO, but focuses on model outputs.

Definition

GEO (Generative Engine Optimization) is the practice of making your brand correctly named, cited, and explained in AI answers from ChatGPT, Gemini, Perplexity, Claude, YandexGPT, and Alice. Unlike classic SEO, you optimize answer quality, not position #1. That shifts the work to crawlability, structured data, source authority, and factual page design.

GEO scorecard example

KPI	Baseline	Day 30	Day 60	Day 90
Mention rate	18%	27%	35%	39%
LLM-Score	34	42	49	53
Trusted citation share	12%	17%	24%	29%

Mini chart (LLM-Score trajectory):

D0  34  ▇▇▇▇▇
D30 42  ▇▇▇▇▇▇▇
D60 49  ▇▇▇▇▇▇▇▇▇
D90 53  ▇▇▇▇▇▇▇▇▇▇

This tells leadership the program is compounding, not spiking from one campaign.

How it works in practice

A practical GEO program has four lanes: technical access (robots.txt, llms.txt, schema, canonicals), answer-first content (clear definitions, comparison tables, dated FAQs), authority (credible third-party citations), and measurement (LLM-Score, Share of Voice, Sentiment). If one lane is missing, performance usually plateaus.

90-day rollout map

Phase	Main work	KPI to watch
Weeks 1-2	Crawl/access fixes	% prompts with any mention
Weeks 3-6	Answer-first page rewrites	LLM-Score and contradiction rate
Weeks 7-10	Third-party authority	Citation share from trusted media
Weeks 11-12	Prompt-pack stabilization	SoV stability across models

Example outcome

A team unblocked GPTBot in robots.txt, rewrote five commercial pages in an answer-first layout, and published three long-form pieces on a major tech media site. After ~60 days they saw 23 mentions in ChatGPT out of 50 test prompts versus zero at baseline, and referral traffic from Perplexity rose ~+340%.

How to read it

GEO is a workflow, not one metric. You know it works when mentions become more accurate, contradictions drop across models, and citations shift toward your controlled sources.

First steps in GEO

Audit robots.txt for accidental blocks on GPTBot, PerplexityBot, and ClaudeBot — roughly 35% of RU sites still disallow AI crawlers from legacy rules.
Run a free Getllmspy check to capture your current LLM-Score baseline.
Rewrite one hero page answer-first: definition in paragraph one, FAQ at the bottom.
Ship Article + FAQPage JSON-LD on that page and validate structured data.
Add one authority campaign (expert quote, original data, or partner publication) tied to a specific prompt cluster.

GEO operating cadence benchmark

Team maturity	Typical cadence	Common failure mode
New team	Monthly check only	Too slow to detect regressions
Growing team	Bi-weekly checks + monthly summary	Inconsistent prompt pack
Mature team	Weekly checks + quarterly narrative	Optimizing dashboards, not source quality

A mature GEO loop keeps one stable measurement core and rotates experiments in separate packs.

GEO vs AEO and LLM SEO

AEO is broader (includes AI Overviews and Copilot surfaces). LLM SEO is often used as a near-synonym. GEO is typically used when the focus is standalone generative products.

When to use

When you are writing a strategy deck for leadership.
When you specifically mean ChatGPT/Perplexity/Gemini presence and not SERP rankings.
When you want to separate "classic SEO" budget from "LLM visibility" budget.

AEO (Answer Engine Optimization)LLM SEO LLM-Score RAG (Retrieval-Augmented Generation)llms.txt

Start your GEO with a visibility baseline