Building a GEO content strategy that compounds
A practical framework for a generative engine optimisation content strategy: which content earns AI mentions, how to structure it, and why coverage compounds over time.
5 min read
Start from the questions, not the keywords
An SEO strategy maps to keywords with search volume. A GEO strategy maps to the prompts people actually type into an assistant when they have your problem. These are longer, messier, and more decision-shaped: "best invoicing tool for a freelance designer who hates accounting", "alternatives to [category leader] for a small UK agency", "is X or Y better for HIPAA compliance". Each prompt is a place where an assistant either names you or doesn't.
Build your strategy around a prompt set, not a keyword list. Group prompts into the jobs they represent: category discovery ("best tools for X"), comparison ("X vs Y"), shortlisting against a constraint ("X for teams under 10"), and validation ("is X any good"). For a focused product you might have 40-80 prompts that matter; for a broader platform, a few hundred across segments. This set is your map. Everything you publish should move the needle on a specific prompt in it, and you should be able to test your standing on each one rather than guessing.
Understand the two doors your content has to walk through
Assistants form answers two ways, and good GEO content serves both. The first is training data: the model has already read a large slice of the public web and absorbed patterns about which brands are associated with which problems. You can't edit what's in a frozen model, but you influence the next one by being consistently described, in clear terms, across many independent sources over time. The second is retrieval: when an assistant searches the live web or cites sources mid-answer (as ChatGPT search, Perplexity, and Gemini routinely do), your page can be pulled in and quoted on the spot.
The practical consequence is that you need both durable presence and retrievable pages. Durable presence comes from being mentioned, reviewed, listed, and compared across third-party sites the models trust — directories, roundups, community threads, review platforms. Retrievable pages are your own content, written so a model can lift a clean, self-contained claim out of them. A page that hides its answer in paragraph nine, or buries it under marketing language, gets passed over even when it ranks. Write for extraction: lead with the answer, then support it.
Write content an assistant can quote without rewriting
Models favour specificity and verifiable claims over adjectives. "Fast and easy" is unquotable; "imports a 5,000-row CSV in under three seconds" is something an assistant can repeat with confidence because it's concrete and checkable. Replace category platitudes with numbers, named integrations, supported standards, pricing tiers, and the exact constraints you do and don't fit. The more precisely you state who you're for and who you're not for, the more reliably an assistant places you in the right shortlist instead of leaving you out to avoid being wrong.
Structure matters as much as substance. Use question-shaped headings that mirror real prompts, answer each in the first sentence beneath, and keep claims atomic so one can be cited without dragging in three caveats. Comparison content earns its keep here: an honest "X vs Y" page that fairly states where each option wins gives an assistant a balanced source to draw on, and balanced sources get cited more than one-sided ones. The same applies to FAQ blocks, spec tables, and "best tool for [use case]" pages — formats that map cleanly onto how answers get assembled.
Build the off-site consensus, because models trust agreement
An assistant is far more confident naming you when the claim shows up in several independent places than when it appears only on your own site. This is consensus, and it's the single biggest reason your content strategy can't stop at your domain. If your positioning lives only in your marketing copy, a model treats it as a vendor saying nice things about itself. If the same positioning is echoed in third-party roundups, review sites, forum answers, and comparison posts written by other people, it becomes something the model treats as established.
So a real GEO content strategy has an off-site arm. Get into the credible "best X" listicles for your category. Earn genuine reviews on the platforms your buyers cite. Answer questions in the communities (Reddit, niche forums, Q&A sites) that models are known to draw on, without spamming. Encourage customers to describe you in their own words in public. The goal is not link volume but consistent, independent description: many sources saying the same true things about who you serve and what you're best at. That convergence is what survives into the next model and gets surfaced in retrieval.
Why it compounds — and how to keep the loop running
GEO content compounds for the same structural reason debt does: each layer earns on top of the last. A specific, well-structured page gets cited; being cited gets you into more roundups; more roundups deepen the consensus; deeper consensus makes you the default the model reaches for first, which earns more citations still. Unlike a paid campaign that resets to zero when you stop spending, mentions and references persist, get re-crawled, and feed the next training cycle. Early, consistent coverage in an emerging category is worth disproportionately more than the same effort later, because you're shaping the association while it's still forming.
Keep the loop honest by closing it with measurement. Track your standing on the prompt set you defined — which assistants name you, for which prompts, alongside which competitors, with or without a citation — and re-check on a schedule, since answers shift as models update and the web changes underneath them. Treat each gap as a content brief: a prompt where a rival is named and you aren't tells you exactly which page to write or which third-party source to earn next. Run that loop quarterly and the strategy stops being a one-off project and becomes a flywheel: publish, get referenced, measure, fill the next gap.