A large language model (LLM) is a neural network trained on vast amounts of text that models language statistically: it predicts the most likely continuation token by token — and can thus understand, summarise and translate text, write code and answer questions. LLMs are the foundation of services such as ChatGPT, Claude and the copilots in office tools.
For companies, LLMs are simultaneously a productivity lever and a governance topic: they measurably accelerate knowledge work but bring their own risks — from hallucinations via data leakage to new attack surfaces such as prompt injection.
How LLMs work — in brief
The technical basis is the transformer architecture: texts are split into tokens, represented as vectors and related via attention mechanisms. In pre-training the model learns language patterns from billions of documents; fine-tuning and alignment then direct it towards helpful, safe behaviour. The context window limits how much text the model can consider per request.
Important for expectation management: an LLM is not a reference work but a probability model — it can produce convincingly worded misinformation (hallucinations) and only knows the state of its training. It becomes reliable in enterprise use through connection to your own vetted knowledge sources (RAG) and human control of critical outputs.
Enterprise use: the switch points
- Sourcing model: APIs of large providers, European hosting or self-operated open-weight models — depending on privacy and cost.
- Privacy: clear rules which data may go into which models; check DPAs and processing locations.
- Grounding: RAG instead of blind trust — answers from own sources with citations.
- Governance: make usage visible (shadow AI) and map EU AI Act duties.