In One Sentence
LLM (Large Language Model) is an AI model trained on a massive volume of text from across the internet, capable of understanding and generating natural-sounding language like a human. It is the "brain" behind ChatGPT and Claude.
What does this look like in practice?
For example, ask ChatGPT "Tell me about Edo-period food culture" and it returns natural-sounding writing as if a human had answered.
That is because the LLM inside ChatGPT (specifically GPT-4 and the like) is "predicting" and generating the answer best suited to your question from the trillions of words it has seen during training.
Why it matters (in a GEO context)
The "AI search" services GEO targets (Claude / ChatGPT / Gemini) all run on top of LLMs.
Understanding LLM characteristics clarifies why GEO tactics make sense:
- They "know" information included in training data: so publishing public information is foundational
- They more easily cite structured information: so Schema.org and FAQ pay off
- They tend to favor authoritative sources: so first-party data and specialist sites are rewarded
Major LLMs
| LLM | Provider | Where it is used |
|---|---|---|
| GPT-4 / GPT-4o, etc. | OpenAI | ChatGPT / SearchGPT |
| Claude | Anthropic | Claude.ai |
| Gemini | Gemini / AI Overview | |
| Llama | Meta | Open source |
What GEO tactics observe is the citation behavior across these LLM-based services.
Limitations of LLMs (worth knowing)
- There is a training-data cutoff: They do not know the latest information past the cutoff date
- Web-search features have been added to compensate (this is "AI search")
- When AI search references the web, becoming the cited side is the goal of GEO