When a language model sits between the question and the answer, being found stops being about keywords and starts being about meaning.
LLM search is search powered by large language models — systems that understand a question’s meaning and generate a direct answer, rather than matching keywords and returning a list of links. When you ask ChatGPT, Perplexity, or an AI-powered search a question, an LLM interprets your intent, draws on relevant sources, and composes a response. It’s the technology underneath the answer engines reshaping how people find information.
The shift is from text-matching to meaning-understanding. An LLM grasps that two differently-worded questions mean the same thing, and that a passage answers a question even if it doesn’t share the question’s exact words — which changes what it takes to be found.
An LLM is trained on vast text and learns the relationships between words, concepts, and ideas — enough to interpret a question and generate fluent, relevant language in response. In LLM search, that capability is paired with retrieval: the system finds relevant sources and the model synthesizes them into an answer. The model understands the question, the retrieval supplies current and specific information, and the answer combines both. This is why being retrievable and clearly answering the question both matter — one gets you into the sources, the other gets you into the answer.
Because LLM search understands meaning rather than matching keywords, being found shifts from targeting phrases to answering questions clearly. Content that directly and specifically answers the real questions people ask — from a source the model can retrieve and trust — is what surfaces in LLM-generated answers. The keyword-matching tactics of older search matter less; clarity, directness, and trustworthiness matter more. Understanding that an LLM sits between the question and the answer is the key to understanding why AEO works the way it does.
Search powered by large language models that understand a question's meaning and generate a direct answer, rather than matching keywords and returning links. It's the technology underneath modern answer engines.
Regular search matches keywords and ranks pages; LLM search understands intent and synthesizes an answer from sources. It grasps meaning, recognizing that differently-worded questions can mean the same thing.
Answer the real questions people ask clearly and specifically, from a source the system can retrieve and trust. Because LLMs understand meaning, clarity and directness matter more than keyword matching.
We test how LLM-powered search responds to your buyers' questions and whether your content is surfacing in the answers.