Two entirely different disciplines share this name, and the confusion costs marketing teams real time.
To a machine learning engineer, LLM optimization means making a large language model run faster and cheaper — reducing inference latency, managing memory, quantizing weights, squeezing more throughput out of a GPU.
To a marketer, LLM optimization — usually shortened to LLMO — means improving how often and how accurately a brand is retrieved, cited and described when those models answer questions.
Same three words. No overlap in practice, tooling, budget or team. If you searched this term expecting the second and landed on articles about KV caches, this is why.
This article separates the two, then covers the marketing discipline properly: what it involves, how it relates to GEO and AEO, how to measure it without overclaiming, and the strongest argument that it is not yet a real discipline at all.
The evidence that this collision is real
We can put a number on it rather than asserting it.
Building the keyword research for this article, we pulled the "llm optimization" seed from Semrush's US database and reviewed every returned query. The overwhelming majority carry machine-learning intent: inference optimization, latency optimization, KV cache, quantization, GPU throughput, llm training vs inference, optimize llm inference, llm inference performance engineering best practices. Marketing-intent variants — llmo large language model optimization, what is llmo in seo — are a small minority of the returned set.
The head term itself carries a Semrush keyword difficulty of 48 with 880 US monthly searches, and the page competing for it is not a marketing page. The canonical result for the engineering meaning is NVIDIA's "Mastering LLM Techniques: Inference Optimization," which walks through the prefill and decode phases of generation, explains why decoding underutilises a GPU, and covers TensorRT-LLM. It is an excellent article. It is also completely useless to a CMO.
That mismatch is the actual finding. A marketer researching this term is not underserved by bad content — they are served excellent content from a different field.
What engineers mean by it
Worth understanding at a surface level, because it is what your technical colleagues will assume you mean.
Generation happens in two phases. In the prefill phase the model processes your input tokens in what is effectively a highly parallel matrix operation that saturates the GPU. In the decode phase it generates output tokens one at a time, each depending on all previous states — a matrix-vector operation that underutilises the hardware. A token is roughly four English characters.
Optimization work targets that second phase: caching intermediate key and value states so they are not recomputed, reducing numeric precision through quantization, batching requests, and managing memory. The goal is cost and speed. Nobody in that conversation is thinking about your brand.
You do not need to know more than this. You do need to know it exists, because it explains why half your search results are irrelevant and why asking an engineer to "help with LLM optimization" will produce a confusing meeting.
What marketers mean by it
The marketing discipline is concerned with a different question: when one of these systems assembles an answer in your category, does it reach for you, and does it describe you correctly?
Three components carry the work.
Retrievability. Whether your content can be parsed and a clean claim extracted from it. This is mostly ordinary technical and editorial hygiene — content in text rather than images, clear structure, claims stated plainly enough to be quoted without distortion. A pricing page saying "flexible pricing tailored to your needs" cannot be quoted by anything.
Corroboration. Whether your claims appear anywhere other than your own site. A claim that exists only on your domain behaves like marketing copy. One corroborated by sources the systems already draw on behaves more like a fact. Both major SEO platforms have built product around this premise — Semrush documents Source Opportunities, sources cited in AI answers that mention competitors but not you, and Ahrefs tracks Citations, how often AI links to your pages or domains as a source.
Consistency. Whether your business is described the same way everywhere it appears. Contradictory service descriptions and stale positioning produce confidently wrong answers, and they are the cheapest category of problem to fix.
What the work actually looks like week to week
The abstraction above hides how ordinary the work is. In practice a quarter of LLMO work breaks down roughly like this.
Auditing what is currently said. Running a versioned prompt set across your target engines and recording, verbatim, how your business is described. This surfaces the accuracy problems, and it is the step most teams skip because nobody has thought to look.
Tracing wrong answers to their source. When a system says something incorrect, it got it somewhere. Usually an outdated page of yours, a stale directory profile, or a third-party article written before a repositioning. The tracing is detective work and it is where most of the early value sits, because a single stale source can be feeding multiple wrong answers.
Rewriting for extractability. Taking the pages that matter commercially and making their claims quotable — a plain statement under a clear heading beats the same information distributed across three paragraphs of narrative. This is editorial work, not technical work, and it is usually the largest single line item.
Earning corroboration. Identifying which sources are cited in your category's answers and pursuing presence in them. Semrush surfaces these directly as Source Opportunities; Ahrefs reaches the same conclusion through citation tracking. This runs on other people's timelines and is the slowest component.
Re-measuring. Same prompt set, same cadence, reading whether anything moved. Three cycles minimum before drawing a conclusion.
Nothing in that list is exotic. That is the point, and any vendor presenting it as arcane is inflating the mystery to justify the price.
How LLMO relates to GEO and AEO
Three acronyms, heavily overlapping, boundaries unsettled. Here is the working distinction we use, offered as a practical convention rather than an industry standard — because no industry standard exists.
| Term | Rough scope | Where it is measured |
|---|---|---|
| AEO — answer engine optimization | Being the answer to a direct question | Across answer engines generally |
| GEO — generative engine optimization | Being retrieved and cited inside generated responses | AI Overviews, AI Mode, ChatGPT, Perplexity and similar |
| LLMO — LLM optimization | The model layer specifically: how the underlying models represent and describe your brand | Across model outputs, including ungrounded ones |
Anyone presenting these as three distinct services with three separate price tags is selling packaging. The underlying work — retrievable content, corroborated claims, consistent facts, honest measurement — is largely the same. HubSpot, which sells an AEO product, says something similar in its own guide: SEO and AEO "are complementary and rely on many of the same tactics," and improvements in one often lift the other. When a vendor's own marketing material concedes the overlap, take it seriously.
Our full comparison of the search-versus-generative distinction is in SEO vs GEO, and the AEO definition is covered in the answer engine optimization guide.
When it is worth investing, and when it is not
Three conditions. All three should hold before you fund this as a distinct workstream.
Your buyers research through assistants. Category-dependent and worth verifying rather than assuming. HubSpot's own research reports 42% of CRM software buyers now use AI search as part of their evaluation process (HubSpot, January 2026) — a real number about one category, published by a company selling into it. Treat it as a prompt to check your own category, not as a fact about yours.
You can see a gap. Competitors appearing in answers where you do not, or your brand being described incorrectly. Both are observable in an afternoon using the method in our AI visibility tracking guide. If you cannot find a gap, you do not yet have a problem to fund.
Your foundations are sound. If your important content is locked in images, your site is slow to crawl, or your service descriptions contradict each other across pages, that is the work. Google is explicit that "the best practices for SEO remain relevant for AI features" and that eligibility requires a page to be indexed and snippet-eligible. Nothing exotic sits above that bar.
If any one of these is missing, spend the money on the missing one.
Three misconceptions worth clearing
"It's just SEO." Partly true and worth engaging with rather than dismissing. The on-site retrievability work overlaps heavily with good technical and editorial SEO, and Google explicitly says the fundamentals still apply. What does not overlap: the accuracy audit, source-level corroboration targeting, and a measurement layer built on prompts rather than keywords. Those three are genuinely additive. A programme that only does the overlapping part should be priced as SEO.
"We need to get into the training data." Understandable instinct, largely unactionable. Training corpora are assembled on schedules and criteria no vendor controls, and model cutoffs mean anything you publish today cannot register until a future training run. Broad, consistent public presence helps over years. It is not a quarterly lever, and anyone selling it as one is selling something they cannot deliver.
"There must be a technical file or tag that fixes this." There is not, at least on Google's surfaces, and Google says so in writing: "You don't need to create new machine readable files, AI text files, or markup to appear in these features. There's also no special schema.org structured data that you need to add." We do not ship these and we do not quote for them. The persistence of this belief is a good indicator of how much of the category is still selling on mystique.
How to measure it without overclaiming
Two sources of truth, and they are not equivalent.
First-party. Google states that sites appearing in AI features are "included in the overall search traffic in Search Console," reported within the "Web" search type. That is platform-reported data about your own property. It is the firmest ground available, with one limitation: AI-feature performance is included in your totals rather than isolated from them.
Third-party. Tools like Semrush's AI Visibility Toolkit — a 0–100 AI Visibility Score, $99 per month, 25 tracked prompts at that tier — and Ahrefs' Brand Radar, which runs a documented 455 million-plus prompt index and reports Mentions, Citations, AI Share of Voice and Estimated Impressions. Both are useful. Neither is confirmed by any platform as reflecting how sources are actually selected. They are external observations of model output.
The discipline that matters: say which is which when the number reaches a decision-maker. And where a factor genuinely cannot be assessed, report the range rather than substituting a midpoint — a lower and upper bound with a note on what evidence would close the gap is more useful, and considerably more honest, than a confident point estimate.
Nobody can guarantee a ranking, an indexing outcome, an AI citation or a revenue result. What can be committed to is a hypothesis, the evidence that would confirm or kill it, and the date you will know.
The four numbers worth reporting
Most LLMO reporting is either a single vendor score or an unreadable data dump. Four figures carry almost all the decision value.
Accuracy rate. Of the prompts where you appear, what percentage describe you correctly? This is the number to lead with in year one, because it is the one with a direct commercial cost and the one you can move fastest. It is also the number no vendor dashboard reports by default, because it requires reading the answers.
Appearance rate by layer. Split category-level prompts from brand-level ones. Brand-layer appearance is table stakes. Category-layer appearance is the growth number, because it represents introduction to buyers who did not search for you.
Source concentration. How many distinct sources are feeding answers about you, and are they yours or someone else's? A brand whose answers all trace to one third-party roundup is one editorial decision away from disappearing.
Competitor set. Who else appears in your category prompts, and has that list changed? This is the earliest competitive warning available anywhere, and it typically shows movement before rankings do.
Report those four with their instrument named — manual prompt set or vendor tool, which engines, what cadence — and the report will survive scrutiny. Report a single composite score without its instrument and the first serious question will dismantle it.
The open question: training data or retrieval?
This is the live technical dispute in LLMO, and how you answer it changes what you actually do. It is worth understanding properly because vendors on both sides will sell you a programme based on their answer without ever naming the question.
Position A — parametric knowledge. The model describes your brand from what it absorbed during pretraining. On this view, visibility is a function of how thoroughly and how consistently your business appeared across the public corpus before the model was trained. The work is long-horizon: broad presence, consistent description, earned coverage. You cannot influence it on a quarterly cycle, and a model's cutoff date becomes a meaningful constraint on how quickly anything you change can register.
Position B — retrieval at answer time. The model fetches live sources when assembling the answer, and what it says about you reflects what it just retrieved rather than what it memorised. On this view the work is immediate and controllable: retrievable pages, corroborated claims, consistent facts on sources that get pulled.
The evidence points substantially toward B for commercial queries — and Google has effectively documented it. Google states that AI Overviews and AI Mode may use a "query fan-out" technique, "issuing multiple related searches across subtopics and data sources" to develop a response, and that this surfaces "a wider and more diverse set of helpful links." That is retrieval behaviour described in Google's own words. Google also confirms the eligibility condition is ordinary: a page must be indexed and eligible to appear with a snippet.
Retrieval is also demonstrably expensive, which tells you it is doing real work. NVIDIA's engineering documentation notes that retrieval-augmented generation pipelines "require putting large amounts of information into the input of the model, greatly increasing the amount of processing work the LLM has to do." Systems do not absorb that cost for decoration.
Where it stays CONTESTED. Not every answer is grounded. Systems answer some questions from parametric knowledge alone, with no retrieval step, and which queries get grounded varies by platform and is not published by any of them. A brand that exists strongly in training data has an advantage on ungrounded answers that no amount of on-site work replicates this quarter. Both mechanisms are real; the mix is unknown and platform-specific.
Our read. Work the retrieval layer, because it is documented, controllable and responds inside a quarter — and because Google has told us fan-out is happening. Treat parametric presence as the slow compounding asset that earned coverage and corroboration build over years. The two are not alternatives competing for the same budget; they operate on different clocks, and a programme that only does one is optimising for one half of the mechanism.
What would change our position: platform disclosure of how often commercial queries are answered without retrieval. If that number turned out to be high, the balance shifts materially toward corpus-building work and away from on-site retrievability, and the sequencing in the next section would change with it.
What to do in the next ninety days
First thirty days. Settle your internal vocabulary — pick one term, write down what it means for your organisation, and use it consistently in reporting so your data survives the industry's naming churn. Take a baseline using a versioned prompt set. Audit accuracy: what are these systems currently saying about you, and is it right?
Days thirty to ninety. Fix the accuracy errors first, because they are the cheapest and most damaging. Then address retrievability on the pages that matter commercially — the ones where your claims are currently unquotable. Change one layer at a time and allow three measurement cycles before concluding anything.
Beyond ninety days. Corroboration. This runs on other people's publishing schedules and cannot be compressed, and it is where durable movement comes from.
Where this goes next
Two things are worth building toward, and neither is a subscription.
Build a vocabulary that survives the churn. GEO, AEO and LLMO are competing to name overlapping work, and the keyword data suggests at least one will lose. Organisations that define their own internal terms and measure consistently against them will still be able to read their own three-year trend. Organisations that rebuild reporting each time the industry renames itself will not.
Build content that is easy to quote accurately. Google has confirmed AI Overviews and AI Mode may use query fan-out — issuing multiple related searches across subtopics to assemble a response. The practical consequence is that a page answering a genuine cluster of related sub-questions can be reached from several branches, while a page answering one can be reached from one. That is not an argument for length. It is an argument for completeness within a tight scope, and for stating the things most marketing copy avoids: prices, limitations, definitions, straight comparisons.
The brands that these systems describe accurately in two years will overwhelmingly be the ones that made themselves easy to describe accurately today. The acronym on the invoice will not matter.
If you want to know whether your brand is being retrieved and described correctly — or only indexed — that is what an LLMO audit answers. We have been running search programmes since 2018 across more than 400 brands, and the accuracy problems are almost always cheaper to fix than clients expect.
