Generative engine optimization (GEO)
Updated 2026-09-15
Generative engine optimization is the practice of improving how often, how accurately and how favourably a brand appears inside answers generated by AI systems. It borrows research and content discipline from SEO, but it optimises for inclusion in one synthesised answer rather than for a position in a list of links.
What the work actually involves
GEO work splits into three repeating parts. First, a prompt set that reflects how buyers ask — discovery, comparison, pricing and switching questions rather than brand names. Second, content that an engine can lift a claim from: the answer stated plainly near the top, facts that can be attributed, comparisons that survive being summarised. Third, measurement as a sampled rate across engines, because a single answer proves nothing and non-determinism makes one-off checks misleading.
Illustrative sequence (example programme, not a customer measurement)
A team builds 35 prompts, measures a baseline of mention rate 0.18 and coverage 0.26, finds that 18 prompts never surface the brand, publishes four pages answering exactly those questions, and re-measures the same prompt set a month later. Whether the number moves is the finding; the prompt set staying constant is what makes it a finding at all.
Common mistakes
- Treating GEO as a new keyword list. The unit of work is a question and the evidence behind the answer, not a term with a search volume.
- Publishing content without a baseline measurement, which makes any later change impossible to attribute.
- Promising a percentage outcome. Nobody controls a non-deterministic system they do not own.
Frequently asked questions
- Is GEO replacing SEO?
- No. Grounded AI answers are built from pages that search engines already surface, so classic SEO remains an input. GEO adds the part SEO never had to handle: being quotable inside a generated answer and being measured by sampled rates instead of positions.
- How long before GEO work shows up in measurements?
- Long enough that impatience is the main risk: engines re-crawl and re-rank on their own schedule, and the honest cadence is a fixed prompt set measured every cycle rather than a check the day after publishing.
