What is GEO (Generative Engine Optimization)?
Generative Engine Optimization is the practice of shaping how generative AI models describe and recommend your product. In day-to-day work it’s effectively the same discipline as Answer Engine Optimization.
Updated June 2026
The short definition
Generative Engine Optimization (GEO) is the practice of influencing the output of generative AI engines — ChatGPT, Claude, Perplexity, Gemini — so they represent and recommend your product accurately. The term emphasizes that these systems generate new text rather than return a fixed list, so the goal is to shape what gets generated about you.
GEO vs AEO — is there a difference?
For almost all practical purposes, no. AEO (Answer Engine Optimization) and GEO describe the same work from slightly different angles:
- AEO emphasizes the answer — being named when an engine responds to a buyer’s question.
- GEO emphasizes the generative nature of the engine — influencing the model’s synthesized output.
The tactics are identical in practice: clean structured data, an llms.txt, content built around real buyer questions, and consistent descriptions of your product across the web. Pick whichever term your team prefers — what matters is the work.
Why it matters for B2B SaaS
When a buyer asks a generative engine for a recommendation, the model produces a confident, synthesized answer naming a few products. There’s no list of ten links to scan — just the names it chose. GEO is how you make sure your product is one of the names, and that it’s described in a way that matches how you’d describe it yourself.
How GEO is measured
The same way AEO is: with share-of-answer — the percentage of buyer-intent prompts where the engine names you — tracked over time, across each major model, and tied back to trials and pipeline.