A short, usable definition
LLM Optimization covers the methods that maximise three outcomes: the correct understanding of a site by a large language model, the citation of that site in responses generated by AI engines, and the faithful reuse of its content.
The real scope: which engines, which surfaces
The field isn't monolithic. It covers a family of surfaces that share one trait: they produce a synthetic answer rather than a list of blue links.
| Surface | Engine or product | Dominant mechanic |
|---|---|---|
| AI-assisted search | ChatGPT Search, Perplexity, Gemini, Claude | Real-time RAG, dynamic corpus, visible citations |
| Search summaries | Google AI Overviews, Bing Copilot | Summary injected above classic results |
| Conversational assistants | ChatGPT, Claude, Gemini (without search) | Internal knowledge, depends on training corpus |
| Specialised agents | Copilot Office, NotebookLM, plugins | Targeted content consumption per task |
Optimising a site for these surfaces isn't about hacking a model. It's about making content clear, structured and credible enough that a retrieval system, then a generation model, picks it, extracts it correctly and cites it.
Why now — the usage shift
Three signals converge in 2026:
- AI Overviews are broadly rolled out and intercept clicks on many informational queries.
- ChatGPT Search has been accessible without login since late 2024, and usage keeps growing.
- Perplexity has settled into professional use — B2B in particular — as a Google alternative for dense research.
Clicks to sites are declining on some intents, not all. Transactional and navigational intents keep generating visits; informational intents — where AI Overviews and AI engines answer directly — are transforming. Being cited in the answer becomes as important as being clicked.
GEO, AEO, LLM SEO: the terminology untangled
| Term | Origin | Scope |
|---|---|---|
| GEO — Generative Engine Optimization | Academic literature 2024 (Aggarwal et al.) | Optimisation for generative engines. |
| AEO — Answer Engine Optimization | Legacy SEO ecosystem (pre-LLM) | Optimisation for answer engines, initially featured snippets and voice assistants. |
| LLM SEO / LLM Optimization | Technical and content-design community | Optimisation for language models themselves — retrieval, citation, reuse. |
The main levers, without romance
- Machine legibility — clean HTML, H2/H3 structure, short paragraphs, standalone sentences.
- Entities and disambiguation — naming, repetition, co-occurrence with domain markers.
- Topical authority — cover a subject in depth, interlink smartly, accumulate relevant links.
- Citations and sourcing — cite sources, use dated figures, make content verifiable.
- Technical operations — crawl, rendering, schema, AI bot management.
- Freshness — explicitly date pages, maintain them, prefer persistence over proliferation.
These levers are turned into steps on the method page and organised into a framework on the LOOP page.
What LLM optimisation is not
- It's not a prompt hack. Hiding instructions in a page doesn't work sustainably.
- It's not a ranking promise. No method guarantees a citation — systems are non-deterministic.
- It's not a SEO replacement. It's an extension built on classic SEO foundations.
- It's not just an
llms.txtfile. That file is a marginal component of a broader whole.
Real limits of the concept today
A serious hub must surface its limits. Here's what, in 2026, remains partially or fully out of your control:
- Citation measurement is a proxy, not an exhaustive metric. Answers vary by user, session and model version.
- Training corpora are opaque. A page published today may take 3 to 12 months to become eligible for citation in some models.
- Internal ranking mechanisms of RAG engines (Perplexity, ChatGPT Search) aren't published.
- Corpus biases favour already-installed sources (Wikipedia, authority sites, major media).
Business stakes
For a brand, being cited by AI engines produces three effects:
- Asymmetric visibility — present in an answer at the moment a prospect is searching, without paying per click.
- Proxy trust — being cited by an AI engine counts, rightly or not, as a credibility signal.
- Long acquisition tail — a single well-structured page can be cited for months.
Conversely, not being cited means becoming invisible to a growing share of queries — particularly at the top of the funnel.