Large Language Model (LLM)
A Large Language Model is an AI model that understands and generates natural language based on statistical patterns learned from billions of text documents.
A Large Language Model (LLM) is a machine-learning model trained on vast amounts of text, learning to predict word patterns. The best-known LLMs are GPT (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta) and Mistral. They power the AI search engines GEO targets. An LLM does not produce guaranteed truth but statistically likely language — which explains both its linguistic ability and its tendency to hallucinate facts. Modern AI engines combine LLMs with live search data via RAG and grounding.
Example
When you ask ChatGPT 'Who is Europe's largest Bitcoin hardware supplier?', the engine first searches current web pages, feeds the relevant snippets to the underlying LLM (GPT-5), and turns them into a natural answer with citations. The answer quality depends on how well your page is found and parsed.
Frequently asked questions
How does an LLM choose which sources to cite?
It depends on the engine. 'Bare' LLMs don't cite — they reproduce training data. Engines with live search (ChatGPT Search, Perplexity, AI Overview) pick sources on relevance, authority and freshness. Pages that are easy to parse are chosen more often.
Do LLMs get smarter every day?
No, not automatically. An LLM learns during training; once deployed it doesn't change. Improvements come via new model versions or external augmentation (search, tools, memory).
Related terms
Further reading
- → Our service: GEO
- → Blog: Google March 2026 Spam Update: AI content penalised