What Is an LLM? How Large Language Models Work

01What Is an LLM? A Short Definition

We can answer the question what is an LLM in its simplest form like this: a statistical text-completion engine that has learned the patterns of language by reading billions of sentences. When you type a question, it sees the question as the beginning of a text and produces the most fitting continuation word by word.

The word "large" describes two things: the amount of data used in training and the number of adjustable values inside the model, that is, the number of parameters. Parameters are the numerical form of what the model learned during training.

These models do not store knowledge line by line like a database. They learn it by compressing it into patterns. That is why they can write very fluently but sometimes also produce wrong information with great confidence.

02What Is a Token?

Language models process text not as letters or words but in pieces called tokens. A token is sometimes a whole word, sometimes a suffix and sometimes a single punctuation mark. In agglutinative languages such as Turkish, a single word is often split into more than one token.

The token concept shows up in practice in three places. Model services usually charge per token; the amount of text a model can process at once is measured in tokens; and response speed is expressed as tokens generated per second.

• Analyzing a long contract uses far more tokens than having a short email written.
• The same content usually takes more tokens in Turkish than in English; take this into account when calculating cost.
• Tables, code and special characters also increase the token count.

03What Does the Transformer Architecture Do?

Almost all of today's language models are based on a neural network architecture called the transformer. At the heart of this architecture is the attention mechanism: when processing a word, the model looks at all the other words in the sentence and works out which ones matter most for meaning.

For example, in the sentence "He sat on the river bank, then went to the bank and opened an account", the two different meanings of "bank" are told apart by looking at the surrounding words. Older architectures read text in sequence and lost these relationships in long sentences; the transformer builds these links in parallel.

Being able to work in parallel made it possible to train on graphics cards (GPUs) at large scale. The rapid leap in language models comes from the combination of this architecture and growing computing power.

04How Are They Trained?

An LLM is usually trained in several stages. Each stage shapes a different aspect of the model, and the assistant the end user sees is the sum of these stages.

Pre-training

The model learns to predict the next token in broad text sources such as books, web pages and code repositories. Grammar, general knowledge and reasoning patterns are acquired at this stage. Training data has a cutoff date; the model does not automatically know about events after that date.

Fine-tuning and feedback

The model that comes out of pre-training is a good text completer but not a helpful assistant. Methods such as fine-tuning with question-and-answer examples and reinforcement learning from human feedback make the model follow instructions and give safe, useful answers.

Response generation and temperature

Using the model after training is finished is called inference. At each step the model computes a list of probabilities for possible tokens and picks one of them. The temperature setting determines how free this choice is: a low value produces more consistent and repeatable answers, a high value more creative but less predictable ones.

This is why you get a slightly different answer to the same question each time. Low temperature is preferred for tasks like reports, classification and data extraction; higher temperature for slogans and idea generation.

05The Context Window and Memory

The context window is the total number of tokens the model can see at once in a single request: your message, the earlier conversation, the documents you attach and the model's own reply must all fit in this window. When the window fills up, the oldest information falls out.

The model has no permanent memory; the whole conversation is sent again with every request. The memory features some applications offer are helper layers that store important information separately and add it to later conversations.

For large bodies of information that do not fit in the context window, such as company documents, methods that search for and retrieve the relevant parts are used. This approach is the most common way to get up-to-date, company-specific answers without retraining the model.

06Where Do LLMs Go Wrong?

A language model's goal is not to find the truth but to produce a likely continuation. It can write a fluent answer even on a subject it knows nothing about; this is called hallucination. An invented source, a nonexistent clause of a law or a wrong date are errors of this kind. We covered the causes and ways to catch them in detail in our article on AI hallucination.

• Current information: it does not know developments after its cutoff date unless it is given internet access.
• Calculation: it can make mistakes in long arithmetic; use a calculator tool for exact results.
• Citing sources: when asked, it may make up sources; every link and quotation must be checked.
• Local detail: its knowledge can be weak about small towns, local regulations and niche industries.

07Using LLMs in Business

Positioned correctly, LLMs free up a great deal of team time. The rule is simple: the model produces a draft, a human decides. Writing, summarizing, classification and data extraction are the most efficient uses.

On the marketing side, campaign ideas, ad copy variations and content drafts can be produced quickly; but brand voice and accuracy checks are the job of the digital marketing team. Content produced for search visibility needs to be measured through an SEO and analytics process.

On the software side, LLMs can be connected to existing systems through an API: classifying incoming customer messages, standardizing product descriptions or summarizing reports from order data. Such connections should be planned within e-commerce integration and industry-specific software solutions.

You can find which model stands out at which task in our ChatGPT, Claude and Gemini comparison, and techniques for writing instructions to get better results in our how to write a prompt guide.

08What to Look For When Choosing a Model

There is no single "best model"; you choose according to the type of work. Cost, speed and data policy matter in the evaluation as much as quality.