Large Language Model (LLM)
A large language model (LLM) is an AI system that has been trained on vast amounts of text and learns from it to understand and generate language itself. It calculates, word by word, the most likely continuation of a text. LLMs power chatbots like ChatGPT, Claude, or Gemini and phrase their answers.
Why it matters for your visibility
More and more people no longer ask their questions on Google but directly to an AI assistant. A large language model is always working behind it. It decides which companies, products, or sources appear in an answer and which do not. Whoever is named here gains attention without a single click on a results list being necessary. That is why classic SEO is no longer enough. You have to understand how an LLM takes in and reproduces information in order to appear in its answers at all. This is precisely what generative engine optimization takes care of. It ensures that your knowledge is tangible, understandable, and citable for the model.
How an LLM works
An LLM breaks text into small units called tokens. These can be whole words or parts of words. During training, the model reads billions of such tokens from books, websites, and forums. In doing so, it learns patterns: which words typically follow one another, which terms belong together. When it receives a request, the model then predicts the most likely continuation token by token. It does not understand language the way a human does but calculates with probabilities. The technical basis is usually a transformer model, an architecture of neural networks that is particularly good at recognizing which words in a sentence refer to one another. That explains why LLMs deliver astonishingly fluent, but not always correct, texts.
Common mistakes and limits
An LLM can sound convincing and still be wrong. When it invents facts, this is called a hallucination. The model also only knows what was in its training data, so it has a knowledge cutoff date and knows nothing about events after it, unless it additionally searches the web. For you this means: do not rely blindly on answers and verify numbers, names, and quotes. Conversely: if your website offers clear, well-structured, and factually clean information, the risk that a model tells nonsense about your offering decreases. Unambiguous details about opening hours, prices, or services help the model reproduce you correctly.
Relation to AI recommendations
When an AI assistant recommends a restaurant, a software solution, or a trade business, the underlying LLM draws on patterns from its training data and often on current search results. Brands that are described frequently, consistently, and in understandable language across the web appear with higher probability. This is exactly the lever for your AI visibility. You do not influence the model itself but the material it draws from: your texts, your structured data, your mentions on other pages. The clearer your digital profile, the more likely an LLM is to surface you as a fitting answer. To that end, regularly measure in which answers you are named.
Example
Imagine a small tax advisor looking for new clients. In the past, a prospect would have typed into Google and clicked through the list of results. Today they ask an AI assistant: "Who can help me in Leipzig with the tax return for the self-employed?" The large language model behind it phrases an answer and names specific firms. Whether our tax advisor is among them depends on how well the model knows their offering from the web: clear service descriptions, consistent address data, expert articles on typical questions. Whoever is cleanly set up here gets recommended, without ever having paid for advertising.
Common questions
Is a large language model the same as ChatGPT?
Not quite. ChatGPT is a product, that is, an application with an interface. The large language model is the engine inside it that generates the answers. One and the same model can sit inside different products, and one product can use several models.
Can I influence what an LLM says about my company?
You cannot retrain it directly. But you can influence the basis: clear, current, and structured content on your website as well as consistent mentions elsewhere increase the chance of being reproduced correctly and positively.