# Large language model

> A large language model (LLM) is an AI model, trained on large amounts of text, that processes text inputs called prompts and generates text outputs from them.

Original page: https://t10.de/en/glossary/large-language-model
Publisher: total10 GmbH, https://t10.de/en/glossary

Germany's Federal Office for Information Security (BSI) explains in a [study on generative AI models](https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/KI/Generative_KI-Modelle.pdf?__blob=publicationFile&v=7) (in German) that large language models generate text from correlations learned in training. They use probability distributions to predict which character, word or sequence of words might come next. Article 3(63) of the [EU AI Act](https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:L_202401689) defines a related term, the general-purpose AI model. Among other criteria, such a model is not tied to one purpose, handles a wide range of different tasks competently and can be built into many other systems. Recital 99 cites large generative AI models as a typical example.

A model without access to real-time data has no information about current events, the BSI notes. A shop's current stock levels are not in the model, so the application has to supply them, for instance through RAG. We describe GEO as building content so that language models understand, cite and recommend it.

We use models from external providers and neither run nor train our own. We choose the model for each step: in one project, a small model handles simple tasks, a mid-sized one long prompts and the largest demanding reasoning. We are not tied to one provider, and the model behind an agent can be swapped. Local models are possible; we judge case by case whether one is reliable enough.

## See also

- [AI](https://t10.de/en/glossary/ai): Artificial intelligence (AI) covers methods that let a computer perform tasks which would require intelligence if a person did them.
- [RAG](https://t10.de/en/glossary/rag): Retrieval-augmented generation (RAG) is a method that lets a generative AI model draw on documents held in a knowledge base and use them to enrich its output.
- [Prompt](https://t10.de/en/glossary/prompt): A prompt is the input a user gives a generative AI system, which then responds with an output.
- [Hallucination](https://t10.de/en/glossary/hallucination): A hallucination is content that an AI model makes up while generating its output and that was part of neither its input nor its training data.
- [SEO](https://t10.de/en/glossary/seo): SEO, or search engine optimisation, is the work of helping search engines understand a site's content and helping people find the site through search and decide whether to visit it.
- [GEO](https://t10.de/en/glossary/geo): GEO, or generative engine optimisation, means making content more likely to appear in the answers given by generative AI search.

## Where the term appears

- [Custom AI agents for businesses](https://t10.de/en/ai-agents)
- [Out of the inbox: a knowledge platform for a pharma logistics provider](https://t10.de/en/blog/knowledge-platform-pharma)
- [AI product copy and SEO content with RAG](https://t10.de/en/seo)
- [The self-driving content machine](https://t10.de/en/blog/self-driving-content-machine)

## Sources

1. [Generative KI-Modelle: Chancen und Risiken für Industrie und Behörden, Version 2.0 vom 17.01.2025](https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/KI/Generative_KI-Modelle.pdf?__blob=publicationFile&v=7), Bundesamt für Sicherheit in der Informationstechnik (BSI), accessed 2026-11-07
2. [Verordnung (EU) 2024/1689 des Europäischen Parlaments und des Rates vom 13. Juni 2024 zur Festlegung harmonisierter Vorschriften für künstliche Intelligenz (Verordnung über künstliche Intelligenz), ABl. L, 2024/1689, 12.7.2024, deutsche Fassung](https://eur-lex.europa.eu/legal-content/DE/TXT/HTML/?uri=OJ:L_202401689), Europäische Union, Amtsblatt (EUR-Lex), accessed 2026-11-07