What LLM SEO means
LLM SEO (sometimes LLMO) is generally used as the umbrella term for optimizing toward language models. It covers GEO and AEO and adds a dimension neither fully addresses: what the model already believes about you without searching.
There are two routes into an answer. Live retrieval means the model searches, fetches pages, and cites what it found — rewarding crawlability, structure, and freshness. Model memory is the compressed impression formed from everything written about you before the training cutoff.
You can't edit model memory. You can only influence what exists to be trained on, which makes it a slow, compounding game measured in quarters rather than weeks.
Why it matters for AI search
Most brands optimize for neither pathway, because most SEO work targets a ranking algorithm that behaves like neither. That leaves an opening.
It also explains a common frustration: a brand fixes its technical foundation, starts appearing when models search, and still gets described with two-year-old information when they don't. Those are different problems needing different work.
See it in action
Ask a model about a brand twice — once with search enabled, once from memory.
Same brand, same question, two different answers.
Retrieval problems are fast and technical. Memory problems are slow and depend on the wider web catching up. Most programmes need both, weighted by how established the brand already is.
How to get it right
Working both pathways
- Verify what each model-feeding crawler actually receives — retrieval is the pathway you control directly
- Build entity clarity so a model can resolve your brand rather than guess at it
- Test systematically how each model describes you, and trace errors back to their probable source
- Earn consistent third-party references; agreement across independent sources is what shifts memory over time
- Keep high-value pages current and clearly dated, since staleness pushes retrieval toward competitors
Common questions
Can you get a brand into a model's training data?
Not directly, and nobody can. Training datasets aren't something an agency submits to. What you influence is what exists to be trained on — the volume and consistency of accurate information about you across the open web.
Anyone claiming they can insert your brand into training data is selling something that doesn't exist.
Why do models describe our company incorrectly?
Usually one of three reasons: outdated information that was accurate when written, inconsistent descriptions across your own properties, or confusion with a similarly-named entity.
All three are addressable, but they need diagnosing first — the fix differs completely depending on which one it is.
Is LLM SEO different from GEO and AEO?
It's the broader umbrella. GEO emphasises citation within generated answers; AEO emphasises being the extracted answer; LLM SEO covers optimizing toward language models generally, including model memory.
In practice the underlying work overlaps heavily. What differs is emphasis, not the toolkit.
Does llms.txt matter for this?
It's an emerging convention with genuinely uncertain adoption. Implement it because it's cheap and harmless, but it currently does far less than robots.txt directives and rendering strategy.
Treat it as a hedge, not a strategy.
Related concepts
These come up alongside LLM SEO constantly.