The work is broader than adding keywords to a page. It connects conventional search foundations with answer-level research: what people ask, which sources the system uses, which brands it names, what it says about them and whether that answer leads to a useful business action.
Different names, overlapping work
| Term | Typical emphasis |
|---|---|
| LLM search optimization (LLMO) | Discovery and citations in LLM-driven search and answer systems |
| Generative engine optimization (GEO) | Visibility in generative search experiences |
| Answer engine optimization (AEO) | Direct answers, including—but not limited to—LLM systems |
| AI SEO / LLM SEO | A shorthand that stresses continuity with search-engine optimization |
| AI visibility | The measurement layer: mentions, citations, accuracy and share of voice |
The terminology is not standardized. A provider can use any of these labels while offering only monitoring, only content or a full service. Evaluate the method and deliverables.
What LLM search optimization includes
Prompt and answer research
Build a repeatable set of questions by audience, intent and buying stage. Record which brands appear, which URLs are cited, what claims are made and how answers vary across engines and repeated runs.
Technical eligibility
Ensure that important pages return successful responses, are not blocked by robots directives or noindex, contain meaningful HTML, use clear canonicals and appear in a working internal-link and sitemap structure. Search-specific crawlers such as OAI-SearchBot, Claude-SearchBot and PerplexityBot should be able to access public content when visibility is the goal.
Answer-ready information
Make definitions, facts, comparisons, methods, limitations and evidence clear enough for a reader to verify and a retrieval system to select. This is good editorial structure, not a requirement to write robotic fragments.
Entity consistency
Keep names, descriptions, relationships, locations, product facts and other stable information consistent across the site and reputable external profiles. Structured data can reinforce facts that are visible on the page; it should not invent facts or make hidden claims.
Authority and corroboration
Publish information worth referencing and earn legitimate coverage, listings, reviews and discussion in sources that matter to the category. First-party claims become more credible when independent sources support them.
Measurement and iteration
Track a stable baseline over time, connect observed changes with shipped work and keep human review in the loop. Answer engines and indexes change, so the process is cyclical.
How it differs from—and depends on—SEO
| Question | Conventional SEO view | LLM search view |
|---|---|---|
| Primary observation | Page rankings, impressions, clicks and conversions | Answer mentions, citations, accuracy, share of voice and referrals |
| Unit of research | Queries and result pages | Prompts, generated answers and the sources behind them |
| Competitive set | Domains ranking for a query | Brands named and sources cited, which may be different groups |
| Content goal | Satisfy search intent and earn visibility | Also supply clear, supportable information an answer can use |
| Off-site work | Reputation, coverage and links | Also map which third-party sources shape category answers |
Google’s official guidance is direct: its generative search features rely on core Search ranking and quality systems, so foundational SEO remains relevant. There is no reason to choose between a technically healthy, helpful site and AI visibility work.
Five useful measurements
- Mention rate: how often the brand is named in the monitored answer set.
- Citation rate: how often the brand’s domain or a source supporting it is linked.
- Share of voice: the brand’s portion of named recommendations relative to competitors.
- Answer accuracy: whether pricing, capabilities, positioning and other material facts are correct.
- Qualified referral outcomes: sessions, leads or conversions attributable to AI assistants where measurement is available.
Always retain prompt text, engine, geography or personalization assumptions, date and sampling method. A metric without its test conditions is difficult to compare.
Common myths
“An llms.txt file makes a site rank in AI.”
No universal standard or ranking guarantee supports that claim. It can be a useful map for systems that choose to read it. Google says it ignores the file for ranking. Maintain it as an aid, not a strategy.
“There is special AI schema.”
There is no special schema.org type required for generative AI search. Use supported, accurate structured data that matches visible content.
“More AI-written pages create more AI visibility.”
Volume without original value creates little reason to retrieve a page. Useful experience, evidence and clear editorial judgment matter more than the tool used to draft words.
“A provider can guarantee placement.”
Third-party systems control crawling, indexing, retrieval and answers. Providers can improve the inputs and measure outcomes; they cannot promise a specific answer.
Frequently asked questions
What is LLM search optimization?
It is the practice of improving whether and how a brand or source is discovered, understood, represented and cited in AI-generated answers.
Is it the same as GEO or AEO?
The terms overlap. GEO emphasizes generative engines; AEO covers direct-answer systems more broadly. Compare the actual research, implementation and measurement.
Does it replace SEO?
No. It depends on many of the same crawl, index, quality and reputation foundations and adds answer-level research and source analysis.
Who needs it?
It is most relevant when customers use AI assistants to discover, compare or validate options in your category and when an inaccurate or absent answer carries a real cost.