GEO (Generative Engine Optimization) is the discipline that optimises content and signals for AI generative engines, not for the classic search engine. It appeared strongly as a term in 2024 as ChatGPT, Perplexity, Claude and Gemini started absorbing a significant share of the discovery traffic that used to go to Google.
How it resembles classic SEO
The fundamentals hold: domain authority, content quality, E-E-A-T signals, semantic architecture and schema.org still matter. LLMs are trained on the same public corpus Google indexes, so whatever helps you rank on Google generally helps you get cited by LLMs.
How it differs
- No “position 1”: a ChatGPT response may cite four sources, and they are not ordered as a ranking — they are justified by the context.
- Format matters: clear answers, a first-sentence that answers, concrete data, structured lists. LLMs re-use fragments that are self-contained blocks.
- Freshness with caveats: LLMs have a training cutoff, but many perform real-time retrieval. New content that has not entered the corpus can appear if it ranks well in traditional search.
- Brand weighs more: LLMs prioritise citing brands with strong presence across forums, specialised press and transcribed podcasts, not only on your own site.
How it is measured
Today there is no Search Console for GEO. Three paths: manual prompt audit (run a fixed set of 30-50 prompts monthly on the main four LLMs and log whether you appear), LLM referrals in GA4 (traffic from chat.openai.com, perplexity.ai, etc.), and brand-mention tracking across the public corpus. None replaces the others.