If LLM visibility is the outcome — whether a model mentions your brand — Generative Engine Optimization (GEO), sometimes called Answer Engine Optimization (AEO), is the practice of trying to influence that outcome. This guide covers what GEO actually is, how it differs from the SEO instincts most marketing teams already have, and a set of concrete tactics you can act on — without pretending any of them come with a guaranteed or precisely quantifiable return, because they don’t.
What GEO actually optimizes for
Classic SEO optimizes a page to rank well in a list of links a search engine returns. GEO optimizes the same underlying content — but for a different consumer and a different output shape: a language model that reads, synthesizes, and then rephrases information into a conversational answer, rather than pointing a user at a page to read themselves.
That shift changes what “good content” means in three concrete ways:
- Extractability over ranking signals. A search engine can rank a page well based on backlink authority and technical signals even if the actual on-page text is dense or hard to parse. A language model has to actually read and understand a claim before it can reproduce it confidently — content that states a clear fact in a clear sentence is easier for a model to extract and cite than the same fact buried in marketing prose.
- Third-party corroboration matters more, not less. A model is more confident naming a brand when the same claim about it appears consistently across independent sources — your own site, review platforms, comparison content, documentation, community discussion — because that consistency lowers the risk of the model reproducing something wrong. A single well-optimized landing page, however well-ranked, can’t substitute for that broader corroboration.
- Two distinct influence paths, not one. Some models answer purely from what they learned during training — you can’t influence that on any timeline you control; it updates only when the model itself is retrained. Other models (or the same models in “search” or “grounded” modes) retrieve live web content at answer time — that path responds to content you publish today, once it’s crawled and indexed, on a much shorter timeline. Any GEO plan has to account for both, and for the fact that you often don’t know which path produced a given answer.
Concrete GEO tactics
None of these guarantee a mention — nothing does, because generation is probabilistic and the underlying training/retrieval process isn’t something any single brand controls. They’re the levers that make a mention more likely by making your brand easier for a model to find, trust, and confidently cite.
1. Write for direct extraction
Structure content so a single sentence or short paragraph states a claim completely, without requiring the reader (or the model) to infer context from surrounding paragraphs. A definition, a comparison, or a “how X works” explanation that stands on its own is far easier to lift cleanly into a generated answer than the same information spread thin across a page of narrative copy. This is also, not coincidentally, good writing for human skimmers — GEO rewards clarity that was already a best practice, it doesn’t invent a new one.
2. Keep factual claims consistent everywhere
If your pricing page says one thing and your comparison page implies another, a model that’s seen both is less likely to confidently reproduce either — inconsistency reads as risk. Audit your own public surface area (site, docs, help center, social profiles) for claims that contradict each other, and fix the contradictions before worrying about anything more advanced.
3. Earn genuine third-party coverage, don’t fabricate it
Reviews, comparison articles, and forum discussion written by people who aren’t you are a disproportionately strong signal, because they corroborate a claim without the obvious bias of self-description. This isn’t a lever you flip directly — it’s earned through product quality and outreach, the same way link-building has always been earned, and any content strategy claiming a shortcut here should be treated with suspicion.
4. Answer the actual questions people ask an LLM, not just the ones they type into a search box
The phrasing that works for a search query (“project tracker pricing”) and the phrasing that works for a conversational LLM prompt (“what’s a good tool for tracking project deadlines for a small engineering team”) are often different in structure, even when the underlying intent is the same. Content written to directly answer natural-language, conversational questions — not just short-tail search keywords — is more likely to match how a model’s grounded retrieval or training data represents the question.
5. Use structured data where it’s genuinely applicable
Structured markup (like FAQ or how-to schema) doesn’t guarantee a citation, but it makes the relationship between a question and its answer explicit and machine-parseable, which lowers the work a model has to do to extract a clean answer from your page. Use it where content genuinely fits the format — don’t force a structured-data wrapper around content that isn’t actually structured that way underneath.
Measure before you optimize further
The tactics above are directional, not a guaranteed formula — which is exactly why measurement has to come first, not last. Without a repeatable way to check whether your brand is actually being mentioned more often, in what context, and against which competitors, GEO work is guesswork dressed up as strategy. See our LLM visibility guide for what that measurement loop looks like — phrases, multiple providers, scheduled runs, mention extraction, and trend tracking — and start there before investing heavily in content changes you can’t verify are moving anything.
If you’re already running GEO-flavored content and want to know whether it’s working, that’s precisely the gap LLM Glow’s tracking is built to close: a defined phrase set run on a schedule across providers, so a change in your content shows up as a change in your mention trend, not as a hopeful guess.