LLM
Large Language Model
In one line
A large language model (LLM) is a neural network trained on massive text corpora to understand and generate human language — the engine behind ChatGPT, Claude, Gemini and similar products.
Going deeper
An LLM, or large language model, is a neural network with billions to trillions of parameters trained on enormous text corpora — web pages, books, research, code and dialogue. Given some context, it predicts the next token probabilistically, and that simple loop is what powers ChatGPT, Claude, Gemini and most of the AI products you use day to day. Since late 2022, when ChatGPT went mainstream, the LLM has quietly become the engine behind a new generation of search, content and support workflows.
Under the hood, LLMs are built on the Transformer architecture. Input text is split into tokens and passed through self-attention layers that calculate how every token relates to every other token in parallel. Stack enough of these layers and the model picks up grammar, world knowledge and reasoning patterns purely from statistics. The fluent prose you see in a chat reply is just very fast next-token prediction stitched together.
For marketers, the important shift is that LLMs do not present a list of search results — they generate an answer. Your brand has to live inside the training data, the live retrieval index or the citation pool to even appear in that answer. AI Overviews, ChatGPT replies, Claude responses and Perplexity summaries are all venues where this plays out, and GEO and LLMO are essentially the practice of pulling those LLMs in your direction.
A frequent misread is that LLMs 'know' facts. They do not — they pick statistically plausible next tokens, which is exactly why hallucinations happen. Production systems counter this with RAG, citations, structured data and tool calls layered on top. Treating an LLM as a powerful pattern generator rather than an oracle makes its limits much easier to reason about, and it points you toward the parts of GEO that actually matter.
Two things matter specifically for the Korean market. First, Korean is roughly two to three times more token-heavy than English, so the same amount of content costs proportionally more to run through any LLM. Second, global LLMs see far less Korean web data than English, which means Korean brands have to be more deliberate about the form their information takes online. Treat 'how am I represented to the LLM' as a discipline, not an accident.
Related terms
Transformer
The Transformer is the neural network architecture behind almost every modern LLM, using self-attention to weigh relationships between all tokens in a sequence in parallel.
LLMToken
A token is the basic unit an LLM reads and writes — usually a word or piece of a word. LLM pricing and context limits are all measured in tokens.
LLMContext Window
The context window is the maximum number of tokens an LLM can take in at once — it defines how much content the model can consider in a single prompt.
LLMRAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
GEO·AEOLLMO
LLMO (Large Language Model Optimization) is the work of shaping content, data and context signals so that LLMs understand and cite your brand correctly.
LLMMoE
MoE (Mixture of Experts) is an LLM architecture that activates only a subset of many smaller 'expert' networks per token — letting teams ship bigger models at roughly the same compute cost.
LLMKnowledge Cutoff
Knowledge cutoff is the most recent date covered by an LLM's training data — the reason an AI may not know your current pricing, policy changes or newest products.
GEO·AEONLU
NLU (Natural Language Understanding) is the field focused on machines interpreting human language intent and context — the foundation under intent analysis and query handling in AI search.
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