What is an LLM?
Author: Domeny.ForSale editorial team Published
In brief: An LLM, or large language model, is an artificial intelligence system trained on large datasets that can process and generate language. It can answer questions, summarise documents, translate, and help write text and code. It generates answers based on learned patterns and the context it receives, so fluent, convincing text is no guarantee of truth.
What does LLM mean, and how does it differ from a chatbot?
LLM stands for Large Language Model. “Large” refers to the scale of the model, including the number of its parameters — numerical values adjusted during training — and the scale of the data and computation used to train it. There is no single, universally accepted threshold at which a model becomes “large”.
A language model is a mechanism that processes content, while a chatbot is an application that enables conversation. A chatbot may use an LLM, but it also includes an interface, instructions and additional features, such as searching the internet or reading files. Not every chatbot uses an LLM: simpler solutions operate according to predefined rules.
An LLM is not the same as a search engine, either. A search engine finds materials, whereas a model generates an answer. An application can combine both mechanisms, but access to a model alone does not mean access to up-to-date information from the web.
How does a language model generate an answer?
During training, the model learns patterns in the data: sentence structures, the meanings of words in different contexts, styles of expression and patterns for solving tasks. Many modern LLMs use the transformer architecture, which allows them to account for relationships between parts of the content being processed.
Text is divided into tokens, units that may be parts of words, whole words or punctuation marks, for example. During generation, the model calculates probabilities for successive tokens and uses them to build a response step by step. It does not have to copy a ready-made sentence from its training materials.
The output is influenced by the prompt, meaning the instruction given to the model along with the material to be processed. The application’s instructions and the available conversation history also matter. All this information must fit within the context window — the limited amount of content the model can take into account in a given call.
An ongoing conversation does not automatically mean that the model is being permanently trained further. Application memory and the use of conversations for later training are separate mechanisms that depend on the service and its settings. A long document, meanwhile, may exceed the available context capacity, causing some of its content to be left out.
What is an LLM useful for in practice?
An LLM is particularly useful when you need to transform existing material or prepare a first draft. It can shorten meeting notes, organise customer enquiries, suggest a structure for instructions or explain a piece of code. In tasks like these, it is easier to compare the output with the input material than to assess an answer to a question in an unfamiliar field.
For example, a shop owner has a product description from the manufacturer and needs a short version for customers. Instead of writing “create a good description”, they can use this instruction:
Based on the specification below, prepare a product description of up to 120 words. Use simple language. Do not add features, certifications or uses that are not included in the material. List missing information separately as questions for the manufacturer.
Once the text has been generated, it must be compared with the original, particularly the dimensions, materials, compatibility and conditions of use. The model helps with editing, but does not replace confirmation of the product’s specifications.
When choosing a tool, it is best to test it on a few of your own tasks. The quality of its Polish, document handling, response time, cost and data protection policies may matter more than the size of the model alone. Current limits and prices should be confirmed in the provider’s documentation.
Why do LLMs make mistakes?
A hallucination is an answer containing invented or unsupported content presented as though it were true. A model may invent a publication, a web address, a software function or a detail of an offer. One reason is that generating plausible-sounding language is not the same as checking facts.
A model may also misinterpret an instruction, overlook an exception in a document or make a calculation error. Knowledge acquired during training does not update automatically, and training data may contain biases and inaccuracies. A confident tone does not indicate that an answer is correct.
Some applications reduce these problems by retrieving relevant passages from documents and providing them to the model as context. This approach is called RAG, or retrieval-augmented generation. It makes it easier to base answers on specific materials, but does not remove the risk of misinterpretation or guarantee that a cited source actually supports the conclusion.
How can you use an LLM safely and effectively?
Match your checking process to the consequences of a possible error. A suggested headline can be assessed editorially; information about taxes, health or current law must be verified using an up-to-date, appropriate source. A model’s answer in these fields does not replace personalised advice from a specialist.
Follow a simple procedure when working:
- Define the task and the audience. Specify the expected length, format and constraints.
- Provide the material. Separate source information from the instruction and ask for gaps to be identified.
- Protect data. Do not send passwords, API keys or confidential documents without appropriate authorisation and without checking the service’s policies.
- Verify the output. Check the numbers, names, quotations and claims on which a decision depends.
To start, choose a short, non-confidential text whose content you know well, and ask for a summary. Comparing the two versions will show whether the tool saves work or omits information important to your use case.
See also: What is a CDN?
Read also
