LLM optimization (LLMO) and AI search optimization (AISO)
LLM optimization (LLMO) and AI search optimization (AISO) are two of several competing names for the same discipline: making content that language-model-powered search systems retrieve and cite. Neither has an authoritative definition or standards body behind it, and neither describes work that differs materially from answer engine optimization or generative engine optimization. The proliferation of acronyms is a marketing artifact, not four distinct practices.
The acronym pile
| Acronym | Expansion | Origin |
|---|---|---|
| AEO | Answer engine optimization | Predates LLMs; grew out of featured-snippet and voice-search work |
| GEO | Generative engine optimization | A 2024 KDD paper by Aggarwal and coauthors |
| LLMO | Large language model optimization | Agency and tool-vendor coinage, no canonical source |
| AISO | AI search optimization | Agency and tool-vendor coinage, no canonical source |
Only GEO has a traceable academic origin. AEO has a long pre-LLM history in search marketing. LLMO and AISO appeared in vendor content and never acquired a definition anyone can point to, which is why two agencies will give you two different scopes for the same acronym.
None of this is a reason to ignore the work. It is a reason to ask what a vendor actually does rather than which acronym they picked.
What the underlying work is, regardless of the label
Strip the naming and every version reduces to four questions:
- Can a retrieval system reach the content at all? Crawler access, server-side rendering, and whether the fact exists as text rather than as an image or a PDF.
- Does the content contain the fact? For catalogs this is almost always the binding constraint. Missing attributes cannot be optimized into existence.
- Is the passage self-contained? A retrieved chunk carries no page context with it, which is why chunking and explicit question-and-answer phrasing matter.
- Is the entity resolvable? Whether a model can tie "the 150W high bay from Northmark" to one specific record, which is an identifier and structured data problem.
Any methodology worth paying for addresses at least three of those. A methodology that only rewrites headlines addresses none.
How to read an LLMO pitch
Two questions separate the useful from the repackaged.
First: what does it measure, and against what baseline? "Cited more often" without a named prompt set, a query count, and a re-run cadence is not a measurement. Generative engines are non-deterministic; the same prompt run twice returns different sources, so a claim built on a handful of prompts describes sampling noise.
Second: does it touch the product record, or only the page? For a distributor, most citation failures come from data that does not exist yet — no material grade, no pressure rating, no branch stock. No amount of on-page optimization creates that value. Copy work sits downstream of enrichment, not instead of it.
The B2B version of the problem
Consumer-facing LLMO advice assumes the product record is already complete and the fight is over positioning. That assumption does not survive contact with an industrial catalog, where a large share of SKUs carry no GTIN, the spec sheet is a 40-page PDF covering twelve part numbers, and stock is a per-branch figure.
So the sequencing inverts. In DTC you write better content about products you already describe well. In distribution you first have to turn documents into fields, then decide what to publish. That is why the practical entry point for most distributors is a catalog readiness audit rather than a content calendar.
Frequently asked questions
What is the difference between LLMO, AISO, GEO and AEO?
Mostly branding. GEO comes from a 2024 KDD paper and AEO predates large language models, while LLMO and AISO are vendor coinages with no canonical definition. All four describe making content retrievable and citable by AI answer systems. Evaluate the methodology, not the acronym.
Is LLMO a real discipline?
The work is real; the term is not standardized. There is no specification, certification, or standards body behind LLMO. Anyone using it is describing their own scope, so ask which specific activities are included before comparing two proposals that share the label.
Which term should we use internally?
Pick one and define it in writing. Most teams settle on AEO because it predates the current wave and is understood by search marketers, or GEO because it has an academic reference. The cost of switching terms mid-program is confusion in reporting, not lost visibility.
Does any AI provider publish optimization guidelines?
Google publishes guidance for its AI features in Search Central, and it explicitly says there are no additional requirements or special optimizations beyond being indexed and snippet-eligible. OpenAI publishes crawler documentation and a product feed specification. Neither publishes anything resembling a ranking guide.