GEO (Generative Engine Optimization) is the practice of structuring content so AI answer engines such as ChatGPT, Claude, Gemini and DeepSeek cite it directly in their responses, rather than just ranking it in a list of blue links. It’s the successor discipline to traditional SEO, built for a world where people ask a chat window instead of a search box.
Why GEO is different from SEO
| Traditional SEO | GEO | |
|---|---|---|
| Success metric | Ranking position | Citation frequency |
| Content shape | Long pages optimized for scanning | Answer-first, direct claims an LLM can quote |
| Distribution | Search index | LLM training + retrieval + live browsing |
| Feedback loop | Weeks (crawl/rank cycles) | Continuous, re-queried every conversation |
What actually gets cited
Across the answers Source collects, three patterns show up again and again:
- Direct, quotable claims near the top of the page, not buried after three paragraphs of preamble
- Tables and lists over dense prose, because they’re easier for a model to lift verbatim
- Content that names the comparison explicitly (e.g. “X vs. Y”) when the query has switching intent
That’s the brief Solve writes every draft against, grounded in the same GEO research this post summarizes, kept current as the underlying platforms change how they cite sources.
How to know if it’s working
You need a way to see, per platform, whether you’re actually showing up, that’s what Operate is for: brand visibility percentage, citation share, and a competitor ranking table, refreshed every time Source runs.