A large language model (LLM), often also referred to as a large language model, is a special class of Artificial intelligence (AI), which has been trained to understand, interpret and independently generate human language. These models belong to the family of Deep learning and are generally based on the so-called Transformer architecture.
The term „large“ primarily refers to two aspects: the enormous amount of training data and the high number of parameters. LLMs are trained on petabytes of text data taken from books, articles, websites and other text sources. During this self-supervised learning process, the models adapt billions, sometimes even trillions, of parameters to capture patterns in the language. One parameter is a numerical value in the neural network that represents the strength of the connections between the neurons and is learned during training.
How Large Language Models work
The functionality of an LLM can be divided into several steps, with the transformer architecture playing a central role. This architecture was developed in 2017 by Google researchers and enables efficient parallel processing of text sequences by using the „attention mechanism“. This mechanism allows the model to evaluate the meaning of each word in the context of the entire sentence or paragraph.
The process of language processing in an LLM typically includes:
- Tokenization: The input text is broken down into smaller units, so-called tokens (words, parts of sentences or individual characters).
- Embedding: These tokens are converted into numerical vectors that capture their semantic meaning and their position in the text.
- Transformer blocks: The vectors pass through several layers of transformer blocks that contain the attention mechanism and feedforward networks. This is where complex linguistic patterns and correlations are extracted.
- Forecast: Based on the learned patterns, the model predicts the next token in a sequence by calculating probabilities for all potential next tokens. This step is repeated to generate coherent and contextual text.
Areas of application and relevance for companies
LLMs have significantly advanced the development of artificial intelligence and are used in numerous areas. For companies, they offer considerable potential for increasing efficiency and opening up new opportunities:
- Text creation and Content generation: LLMs can automate blog posts, Marketing texts, emails, product descriptions or creative content, reducing the effort required for content creation.
- Customer service and Chatbots: By integrating with chatbots or virtual assistants, LLMs can understand customer queries, generate meaningful responses and provide round-the-clock customer support.
- Language translation and summary: They enable precise translations between different languages and can convert long documents into concise summaries.
- Data analysis and knowledge extraction: LLMs can analyze unstructured text data in order to extract relevant information, identify moods (sentiment analysis) or retrieve knowledge from large archives.
- Code generation: Many models are able to create program code in different languages based on natural language input.
Well-known examples of large language models that are used in various applications are models such as GPT-5 from OpenAI, Gemini from Google or Llama from Meta. The ability of these models to adapt to specific tasks (fine-tuning) or to be customized through targeted Prompt Engineering makes them versatile tools in the field of digital transformation.
