What is a Large Language Model (LLM)?

A large language model (LLM) is a type of AI model that is trained specifically on text data – articles, books, and conversations. As a result of training, LLMs can generate human-like texts and code.

How does an LLM work?

The large language model definition goes hand in hand with an explanation of how it works.

An LLM, in its basis, consists of three main components:

Data. The amount of text data that is used to train an LLM is enormous and can get as much as one petabyte, which is one million gigabytes. At the same time, LLMs have a massive number of parameters that let them flexibly execute even the most complex tasks based on training data.
Transformer architecture. Transformers are designed to understand the context of each word in a sentence by considering it in relation to other words.
Training. During training, LLMs learn to predict the next word in a sentence, starting with a random guess and improving their forecasts with every iteration based on adjusting their parameters.

So, an LLM is trained on massive volumes of text data to predict words (or tokens) in a sentence and generate the correct outcome based on the user's request. Further fine-tuning helps to adjust an LLM to narrower tasks based on smaller industry-specific data and human preferences.

Types of LLMs by factor

LLM types can be considered from various perspectives based on different factors.

Availability:

Open-source LLMs, such as Falcon and Mistral, provide publicly available weights so you can customize the model to your needs.
Closed LLMS with no weights released, such as GPT-5 and Claude, are accessed via APIs and require minimal setup.

Function:

General-purpose LLMs, such as GPT, Claude, and Gemini, are trained on broad datasets to perform various tasks within a broad number of topics.
Domain-specific models, such as LegalBERT, are trained for a specific field to deliver tailored responses.

Architecture:

Decoder-only models, the vast majority of all modern LLMs, process input as a part of output, predicting token by token.
Encoder-decoder models like BART use an encoder for processing an input, and a decoder for generating output.
Encoder-only models like BERT are primarily used for understanding rather than generating text.

Capabilities:

Text-only models work with just text inputs and outputs.
Multimodal LLMs can handle text, images, and, sometimes, audio and video.

Deployment:

Cloud-hosted LLMs are accessed via APIs; they are scalable and quick to start.
Self-hosted LLMs are set up on your own infrastructure with more control over handled data and operations.

LLM business applications

The LLM meaning for businesses is connected with a wide spectrum of applications and capabilities to automate and speed up processes.

Customer support gets easier and cheaper with smart chatbots and auto-summaries for managers.
Marketing and sales automation enable personalized product recommendations, content generation, and automated lead qualification.
Internal knowledge management improves onboarding and cooperation with meeting summaries, company knowledge assistants, and internal search.
Software development benefits LLMs in many aspects, from automated documentation to code generation.
Data analysis with LLMs enhances decision-making with report summaries and forecast explanations.
HR management uses LLMs for job description creation, resume screening, and automating recruitment processes.
Legal and compliance risks are mitigated through contract analysis, policy comparisons, and regulatory summaries.
Finance areas leverage LLMs with invoice processing, expense classification, budget explanations, and more.