LLM SEO and LLM optimization: a working definition and a method

"LLM SEO" and "LLM optimization" both describe optimizing a brand for large language models. In the US the two terms now draw comparable volume, with "llm optimization" growing about 42% quarter over quarter, and "llm seo tool" up roughly 339%, according to Google Ads data pulled in September 2026. The vocabulary is settling. The practice underneath it is still widely misunderstood.

Two mechanisms, two very different levers

The first mechanism is what the model learned during training, from a corpus frozen at a cutoff date. On that layer, your brand exists if it was well documented on the open web at the time. You cannot change it in any useful timeframe, and nobody can honestly sell you a way to.

The second mechanism is live retrieval. When a question involves a product, a price or anything recent, the model fetches pages at answer time and composes from what it finds. This is where everything is decided, because this is where work you do today changes the answer tomorrow.

In practice, almost every purchase-intent question triggers retrieval. A shopper asking for a product recommendation gets an answer built from pages fetched live, not from training memory. That is good news: the lever is real and it is short-loop.

What actually drives the answer

Once you accept that the answer is assembled from retrieved pages, the question becomes simple: which pages, and why those. Three factors dominate, and a couple more decide close calls.

• Access: if AI crawlers are blocked, your pages are not candidates at all, whatever they say.
• Factual density: a page stating precise, checkable facts is reusable; an atmosphere page is not.
• Corroboration: a fact on your site that is confirmed elsewhere gets restated with far more confidence than an isolated claim.
• Freshness: on availability or pricing questions, a recent source beats an older one outright.
• Disambiguation: if your brand name collides with a common word, the model needs help resolving it, and clean markup provides that.

What does not work, and will still be pitched to you

Two practices circulate and deserve a warning. One is stuffing a page with instructions aimed at the model, hidden or otherwise. Engines treat that as manipulation and it can work against you. The other is mass-producing generated content to saturate a topic. That raises volume, not perceived reliability, and perceived reliability is exactly what decides a citation.

The method that holds is less dramatic. Make your product facts precise and reachable, check what the engines already answer, fix what mechanically blocks a citation, then measure whether the answer moved. That is the loop Tadow automates. Nothing about it is magic, and its main virtue is that every step of it is checkable.