Generative Engine Optimization (GEO) — sometimes called answer engine optimization (AEO) — is the practice of making your product, docs and content easy for large language models to find, parse and cite. If your buyers are asking Perplexity, SearchGPT, ChatGPT, Claude or Gemini 'what's the best tool for X?', the answer they get is now a distribution channel. GEO is how you show up in it.
For AI-native startups this matters earlier than classic SEO. Your audience already lives inside LLM chat surfaces. A single accurate citation in Perplexity for a high-intent query can outperform a month of organic traffic — and unlike Google's ten blue links, the model usually names one or two sources, not ten.
GEO vs SEO: what actually changes
SEO optimizes for a ranking algorithm that returns links. GEO optimizes for a retrieval-augmented model that returns a synthesized answer with citations. The mechanics overlap (crawlability, authority, structured data) but the optimization target is different: you want the model to quote you, not just rank you.
Three practical shifts: (1) write content that answers a specific question in the first 100 words, because that's the chunk the model is most likely to retrieve; (2) make claims verifiable with numbers, dates and sources, because models prefer citing concrete statements; (3) treat your docs, changelog and pricing page as first-class SEO surfaces — they're what models retrieve when someone asks about your product.
Schema markup that LLMs actually use
Schema.org JSON-LD is still the most reliable signal. Add Organization, Product, SoftwareApplication, FAQPage and HowTo where they apply. Models trained on web crawls weight structured data heavily when disambiguating entities — 'PaidNinjas the agency' vs 'paid ninjas the search term' is a job for sameAs and Organization markup.
For every product or feature page, ship: Organization on the root, BreadcrumbList on every leaf, FAQPage for any Q&A section, and Article with author, datePublished and dateModified on long-form posts. Validate with Google's Rich Results Test before shipping — malformed JSON-LD is silently ignored.
Content structuring for retrieval
LLM retrievers chunk pages — usually 200–500 tokens — and rank chunks independently. Write so each chunk stands alone: a clear H2, a one-sentence answer, then the supporting detail. Avoid burying the answer under three paragraphs of throat-clearing.
Use descriptive H2s phrased as questions when the content is Q&A ('How does answer engine optimization differ from SEO?'). Add a TL;DR at the top of long posts. Keep tables and lists — models cite them more often than prose because they're easy to extract.
Citations, authority and the llms.txt file
Models cite sources they trust. Build authority the same way you do for SEO: original research, named authors, a real Organization with sameAs links to LinkedIn, GitHub and Crunchbase, and inbound links from sites the model already trusts (your customers' case studies, partner pages, podcast appearances).
Ship a /llms.txt at your root — a plain-text manifest that tells crawlers what your product does, what your most important URLs are, and how you want to be summarized. It's a young convention but Perplexity, Anthropic and several open-source crawlers already read it.
How we measure GEO
Weekly: run your top 20 buyer questions through Perplexity, SearchGPT and ChatGPT (with browsing) and log which sources get cited. Track 'citation share' the way you'd track keyword rank — it's the only metric that matters.
Monthly: pull referral traffic from perplexity.ai, chat.openai.com, claude.ai and gemini.google.com in your analytics. It's small today and growing fast. The teams treating it as a real channel now will own the citations when it's 10x bigger.
Where to start this week
Pick the five questions a buyer would ask an AI before evaluating your product. Write one page per question, answer it in the first hundred words, add FAQPage schema, link to the page from your homepage, and submit the URL to Bing (which feeds ChatGPT and Copilot). That's the entire MVP of a GEO program — and it's usually enough to start earning citations within a month.