LLM (Large Language Model)
An LLM (Large Language Model) is a neural network trained on very large amounts of text that models language statistically and thereby understands, summarizes, translates and generates text. LLMs are the technical foundation of chatbots, AI assistants and AI agents.
How does an LLM work?
At its core, an LLM repeatedly predicts the most probable next word fragment (token) — given everything that precedes it. What sounds trivial leads, with sufficient model size and training data, to remarkably capable language behavior: the model learns grammar, domain knowledge, patterns of reasoning and style from billions of text examples, without any of this being explicitly programmed in.
Development typically proceeds in two phases: in pre-training, the model learns the structure of language and world knowledge from large, general text corpora. In the subsequent fine-tuning, it is aligned to desired behavior — for example following instructions, answering helpfully and avoiding harmful outputs. The technical basis of almost all of today’s LLMs is the transformer architecture, which can process relationships between all parts of a text simultaneously.
What can LLMs do — and what can’t they?
LLMs are strong wherever language is the working material: summarizing, rephrasing, translating and classifying text, drafting documents, writing code, answering questions about supplied documents. Just as important is knowing their systematic limits:
| Limit | Practical meaning |
|---|---|
| Hallucination | The model produces plausible-sounding but false statements — especially where knowledge is missing. Critical outputs need source grounding or human review. |
| Context window | The working memory is finite, and answer quality tends to decline as it fills up. Long tasks need an architecture that keeps the context small. |
| Knowledge cutoff | The model only knows what was in the training data. Current or company-internal information must be supplied from outside, for example via RAG. |
| Non-determinism | The same input can produce different outputs. Processes that demand exact reproducibility need additional safeguards. |
None of these limits make LLMs unusable — but they determine how a solution must be built to work reliably.
LLMs in the enterprise
In the enterprise, the model itself is rarely the actual problem — what matters is the integration. Three questions arise in practically every project:
- Data integration: How does the model access the relevant company knowledge? Usually via Retrieval-Augmented Generation (RAG), supplemented where needed by fine-tuning or trained adapters.
- Data protection and sovereignty: May the data go to a cloud provider? If not, the alternatives are operation in the EU, in a sovereign environment or on your own hardware — the models then come to the data, not the other way around.
- Model choice and cost: Not every task needs the largest model. Usage becomes economical when the appropriate model is chosen per task and the toolchain is designed for efficiency.
LLM in practice: the model is only one building block
Whether an LLM creates real value in a company depends, in our experience, less on the model than on the workflow around it: the analysis of which process is worth addressing at all, the clean integration into existing systems and quality assurance, and the question of who reviews the outputs before they take effect. Swapping a model is easy — building a sustainable process around the model is the real work.