Large Language Model (LLM)

Their core capability sounds astonishingly simple: they predict which word – or, more precisely, which linguistic unit – is most likely to come next. When carried out on a massive scale and trained using vast amounts of data, this seemingly simple task gives rise to the ability to write fluent text, answer questions, translate and programme.

The ‘Large’ in the name is to be taken literally: these models consist of billions of parameters – the internally learnt adjustment variables – and have been trained on a large proportion of publicly available text. It is precisely this scale that explains both their impressive capabilities and their characteristic weaknesses.

How an LLM works

At its core, an LLM operates on the basis of probabilities. It breaks text down into small units, known as tokens – these can be whole words, parts of words or characters. Based on the text so far, the model calculates which token is most likely to come next, selects it, appends it and repeats the process. This is how a response is generated, word by word. The underlying architecture is called a ‘Transformer’ and allows the model to establish connections between parts of a text that are far apart – which is why LLMs can maintain context across longer passages.

Crucial to understanding: an LLM ‘knows’ nothing in the human sense and does not look anything up. It generates the statistically most plausible continuation. This explains why it is often brilliant and sometimes confidently wrong.

What LLMs can do – and what they cannot

The most significant pitfall is hallucination: the convincing invention of facts, sources or figures that do not exist. Because an LLM is optimised for plausibility, not truth, a false answer often sounds just as confident as a correct one. Therefore, always check LLM outputs in critical contexts.

A concrete example

A company wants to quickly extract the key points of criticism from long customer reviews. In the past, someone would have had to read through them manually. With an LLM, you simply enter the reviews and ask the model to summarise the most common points of criticism in a structured way. Within seconds, it delivers a useful overview. The employee checks it – by cross-referencing a random sample against the originals – and uses it to continue their work. The LLM has not ‘understood’ the reviews, but has identified the linguistic patterns reliably enough to take some of the workload off the employee’s hands. This three-step process of input, rapid processing and human verification is the typical pattern for the effective use of an LLM.

Deploying LLMs in business

There are several ways to ensure that an LLM goes beyond general responses and is tailored to your own company. The behaviour can be directly controlled via prompts (the input instructions). With Retrieval-Augmented Generation (RAG), you can integrate your own documents so that the model responds based on verified company knowledge rather than free association – often implemented via a vector database. And through fine-tuning, a model can be further trained for specific tasks. Which approach is suitable depends on the use case, the data available and the budget – in many cases, a well-formulated prompt plus integration of the company’s own data is sufficient.

Data protection and accountability

Because LLMs often run as cloud services and are fed with company data, data protection and confidentiality are key issues. Where does the input data go, is it used for training, and how can sensitive material be protected? Reputable guides clarify these points in advance. An easy-to-understand, technically sound introduction to how large language models work and the risks involved is provided by the BSI on generative AI models.

Context

The large language model is the key technology behind the current wave of AI: an astonishingly versatile language tool capable of doing much of what was previously the preserve of humans – but it is neither a repository of knowledge nor a guarantor of truth. Its benefits are realised where its strength (processing language) is used in a targeted manner and its weakness (inventing facts) is mitigated through verification and integration with reliable data. Those who use LLMs with this realistic expectation gain a powerful tool; those who trust them blindly will run into problems.