What is a large language model (LLM)?

Large language model: definition

“Large” refers to their enormous size: LLMs consist of neural networks with billions of parameters and have been trained on huge collections of text. “Language model” describes their core ability—they calculate which continuation of a text is most likely to fit the given context.

This gives rise to remarkably versatile capabilities: summarizing texts, translating, answering questions, writing code or structuring information from documents. Well-known model families are GPT from OpenAI, Claude from Anthropic and Gemini from Google, as well as openly available models such as Llama or Mistral. Newer models process not only text but also images, tables or spoken language.

How an LLM works

Modern LLMs are based on the transformer architecture, which was introduced in 2017. Text is broken down into small units called tokens. During training, the model learns to predict the next token in each case, acquiring knowledge about language, facts and relationships in the process.

It is then further trained with examples and human feedback so that it follows instructions and responds helpfully. In use, the model receives a prompt—the input along with context—and generates a response from it step by step. How much text it can take into account at once is determined by its so-called context window.

Benefits and limits of LLMs

LLMs understand natural language, work in many languages and can be used for a wide variety of tasks without any training of your own. Via APIs, they can be integrated into existing software, workflows and websites. Many tasks can thus be solved with nothing more than a well-worded instruction.

At the same time, they have limits that you should be aware of and plan for in every application:

• Hallucinations: convincingly worded but false statements
• Knowledge cutoff: the training knowledge ends at a certain point in time
• No access to company knowledge without an additional connection, for example via RAG
• Data protection: inputs must not flow to providers in an uncontrolled way
• Costs and response times increase with model size and the amount of text

Real-world example: using LLMs in your business

A sales team receives detailed inquiries by email every day. An LLM summarizes each inquiry, identifies the product, quantity and requested date, and stores the data in the CRM in a structured format. An employee checks the result and replies—much faster than before. The decision remains with the human.

Clear rules are essential here: Which data may be processed, by which provider, under which data processing agreement, and at what point does a human review the output? Nano Concepts integrates LLMs into applications and workflows with data protection in mind and selects the model and hosting to suit the use case.