The essentials
- Definition: an LLM (Large Language Model) is an artificial intelligence model trained on billions of texts to predict what comes next in a sequence of words, which is what lets it understand and produce language.
- Examples in 2026: GPT (OpenAI, ChatGPT), Gemini (Google), Claude (Anthropic), Llama (Meta), Mistral; they power the assistants, the AI Overviews and the answer engines.
- Limits: it doesn't "know", it predicts; it can invent (hallucinate), its knowledge has a cut-off date, and it depends on the sources it is given (RAG, web search).
- For a business: LLMs cite content that is clear, structured, sourced and consistent; that is what GEO is about.
A LLMs (Large Language Model) is an artificial intelligence model trained on vast volumes of text to predict the most likely word following a sequence. From that apparently simple ability flow the understanding of questions, writing, summarising, translation and the apparent reasoning behind assistants such as ChatGPT, Gemini, Claude and Perplexity. LLMs sit at the heart of Google's AI Overviews and of answer engines; understanding how they work is necessary to make a company visible in them, which is the subject of GEO.
How an LLM works
- Training: the model reads trillions of words (web pages, books, code, articles) and adjusts billions of parameters to predict each next word. That phase costs tens of millions of euros and fixes the model's "knowledge" at a cut-off date.
- Alignment: the model is then refined with example conversations and human feedback, so it answers usefully and avoids harmful content.
- Inference: for each question, the model breaks the text into tokens, calculates the probabilities of the next token and produces the answer word by word. It doesn't consult a database of facts: it generates the most plausible text.
- Augmentation: to answer on recent or specific facts, assistants add a web search or a document base at question time (RAG), and cite the sources they find. That mechanism is what makes websites appear in the answers.
The main LLMs in 2026
| Model | Publisher | Where you meet it | Web access |
|---|---|---|---|
| GPT | OpenAI | ChatGPT, Copilot (Microsoft), many applications | Yes, with citations |
| Gemini | Gemini, Google's AI Overviews and AI Mode, Workspace | Yes, Google's index | |
| Claude | Anthropic | Claude, professional tools | Yes, with citations |
| Llama | Meta | Meta AI (WhatsApp, Instagram), open models | Depends on the integration |
| Mistral | Mistral AI (France) | Le Chat, enterprise integrations | Yes |
| Sonar | Perplexity | Perplexity, answer engine | Yes, real-time search |
The limits to know about
- Hallucination: the model produces a plausible but false statement (a figure, a quotation, an invented company), because it predicts rather than verifies. Assistants with web search reduce the risk without eliminating it.
- The knowledge cut-off: without web search, the model knows nothing of what happened after its training.
- Dependence on sources: the quality of the answer depends on the pages found; a company absent from the sources, or described inconsistently, is ignored or badly described. The sources used are covered in the sources generative AI uses.
- Limited context: the model handles a finite amount of text per question; long, badly structured content is summarised or truncated.
- Variability: the same question can produce different answers; visibility is measured by repetition, not by a single test.
What it changes for a business
A growing share of searches goes through an LLM: questions put to ChatGPT, AI Overviews at the top of Google, answers from Perplexity. An LLM doesn't rank ten links; it writes an answer and cites two to five sources. To be among them, content has to be findable (indexable, open to AI crawlers), understandable (a direct answer, a structure of questions, tables, definitions), reliable (a named author, sourced figures, consistency with other sources) and consistent with the company's entity. How answer engines work is covered in AI engines explained, and how to write for them in content for LLMs and content structure for LLMs.
The uses of LLMs in marketing
| Use | What it brings | The precaution |
|---|---|---|
| Writing and rewriting | Drafts, variants, summaries in minutes | Expert review, verified figures, brand voice |
| Data analysis | Explaining a dashboard, spotting anomalies | Accurate data going in, calculations checked |
| Keyword and intent research | Real user questions, grouped | Cross-check with Search Console |
| Customer service and assistants | Round-the-clock answers from a document base | An up-to-date base, escalation to a human |
| Visibility monitoring | Testing what AI says about the company | Repeat the tests, across several assistants |
How GreenRed helps
Rather than juggling several tools, GreenRed's GEO and AI module brings these metrics together in a single dashboard, compares them over time and tells you which actions come first. You can try it free, with no card, from the Pricing.
Frequently asked questions
What is an LLM?
A large language model: an artificial intelligence model trained on billions of texts to predict the next word in a sequence, which lets it understand questions and produce text. GPT, Gemini, Claude, Llama and Mistral are LLMs; they power ChatGPT, Google's AI Overviews, Perplexity and Copilot.
Does an LLM look information up on the internet?
Not by itself: it generates the most likely text from its training. Assistants add a web search or a document base at question time (RAG) and cite the pages they find. That mechanism is what makes a site appear in an answer.
Why does an LLM sometimes get things wrong?
Because it predicts a plausible continuation without verifying the facts: that is hallucination. The risk rises on rare, recent or poorly documented subjects. Web search and citations reduce it; human verification remains necessary for any figure or name.
How do you get cited by LLMs?
By publishing content that is indexable and open to AI crawlers, structured as questions with direct answers, with tables, sourced figures, a named author, and a description of the company that is consistent across every source. That is what GEO is about, as a complement to SEO.