What is GEO?
Generative Engine Optimization, in plain English.
GEO — Generative Engine Optimization is the practice of shaping how large language models like ChatGPT, Claude, Perplexity, and Gemini describe, cite, and recommend your brand when someone asks them a question. If SEO is about ranking on a page of blue links, GEO is about being inside the answer.
Targets Google's index. Success = ranking positions on a SERP. Levers: keywords, backlinks, on-page structure. Feedback loop: Search Console impressions and clicks.
Targets LLM answer engines. Success = citation share across a fixed query set. Levers: SSR content, JSON-LD, llms.txt, ai-agents.json, authority in the pre-training corpus. Feedback loop: repeated polls of ChatGPT / Claude / Perplexity / Gemini.
The three things LLMs actually look at
- 1. Retrievable content. LLM crawlers and their retrieval-augmented layers render the HTML you ship at the URL — not the app that hydrates on click. A JavaScript-only shell is invisible to most of them. Ship real server-rendered HTML.
- 2. Machine-readable structure. JSON-LD (Organization, WebSite, Product, FAQPage, BreadcrumbList), a valid
llms.txtat the root, and anai-agents.jsonmanifest let models parse your site without guessing. - 3. Authority signals. Mentions in the pre-training corpus (Common Crawl, Wikipedia, reputable publications) and live-retrieval indexes (Bing, Google, each vendor's own crawler) still decide who gets cited when the model is unsure.
Frequently asked
What is Generative Engine Optimization (GEO)?
GEO is the practice of shaping how large language models like ChatGPT, Claude, Perplexity, and Gemini describe, cite, and recommend your brand when a user asks a question. Instead of optimizing for a blue-link SERP, you optimize for the answer an AI hands the user directly.
How is GEO different from SEO?
SEO targets Google's ranking algorithm and blue links. GEO targets LLM answer engines, where visibility is measured in citation share across a query set — not in keyword positions. The signals overlap (crawlability, structured data, authority) but the outputs differ: SEO produces rankings, GEO produces citations inside AI answers.
How do LLMs decide which sites to cite?
Each model uses a slightly different mix, but the recurring inputs are: retrievable content (SSR HTML, not JS-only shells), machine-readable structure (JSON-LD, llms.txt, ai-agents.json), authority signals from the pre-training corpus, and live retrieval from Bing / Google / their own indexes at query time.
Is GEO the same as AI SEO or LLM SEO?
Yes — GEO, AI SEO, LLM SEO, and answer-engine optimization all describe the same discipline. GEO is the label most researchers have converged on (originating from a 2023 Princeton paper). We use GEO throughout Solarly.
Can I actually measure GEO?
Yes, but with humility. You poll a fixed set of category questions across the major LLMs on a schedule, log which domains they cite, and track citation share and drift over time. Solarly publishes its full 70+ seed-query matrix and confidence bands at /geo/methodology so the number isn't a black box.
What actually moves the needle?
In our audits, the three highest-leverage changes are (1) a valid llms.txt at the root, (2) SSR-rendered JSON-LD covering Organization, WebSite, and any Product / Service / FAQPage schemas that apply, and (3) an ai-agents.json manifest that names your preferred citation string. Everything else is a rounding error until those exist.
Keep reading
- → How Solarly measures GEO — the full 70+ seed-query matrix and cadence.
- → GEO glossary — llms.txt, ai-agents.json, citation share, confidence band.
- → Free llms.txt generator — build a valid llms.txt in 60 seconds.
- → Case studies — real before/after audits.