What is an LLM (Large Language Model): how large language models work

In recent years, large language models (LLMs) have become the cornerstone of modern AI services. They are used in chatbots, text and code generation, intelligent search, document analysis and corporate assistants. Today, LLMs are used not only by machine learning specialists, but also by developers, marketers, analysts, support teams and business teams.

However, there are still many misconceptions surrounding large language models. They are often simply referred to as ‘artificial intelligence’ or ‘neural networks’, without explaining exactly how they work or where the limits of their capabilities lie. Let’s break down in simple terms what LLMs are, how large language models are structured, where they are used, and why businesses are actively integrating them into their processes.

What is an LLM (Large Language Model)?

An LLM (Large Language Model) is a large language model trained to work with text and natural language. Such models are capable of:

• analysing text;
• understanding the context of queries;
• generating responses;
• writing code;
• translating and summarising documents;
• searching for information based on meaning.

It is LLMs that form the basis of modern chatbots and generative AI.

Why is the model called ‘large’?

The term ‘large’ refers not only to the size of the model, but also to the scale of its training. Modern LLMs contain billions of parameters and are trained on vast amounts of data: books, articles, documentation, websites and code.

This enables the model to recognise complex relationships between words, understand the context of a conversation, and generate coherent responses that resemble human speech.

How LLMs differ from AI and neural networks

These concepts are often confused, although they refer to different levels of technology:

• Artificial intelligence (AI) is a broad field of technology;
• A neural network is a mathematical model within AI;
• An LLM is a specialised class of neural networks designed to work with language and text.

It is important to understand that an LLM does not ‘think’ like a human and does not possess consciousness. The model works with statistical dependencies and the probabilities of text continuation. However, it does so with such precision that the responses appear meaningful.

How large language models work

Modern LLMs are based on the Transformer architecture, which has revolutionised the approach to language processing and enabled models to take long-term context into account.

The Transformer architecture

Most modern language models use the Transformer architecture.

The key feature of transformers is the attention mechanism. It allows the model to analyse the text as a whole and understand which parts of a sentence are related to one another.

For example, the model can correctly determine which word the pronoun ‘he’ refers to, even if the subject was mentioned several lines earlier.

How LLMs are trained

Training takes place in several stages.

Pre-training

In the first stage, the model is fed a vast amount of text and independently identifies patterns in the language. It is not explicitly taught grammar—instead, it learns to predict word sequences.

Fine-tuning with instructions

After the initial training, the model is further adapted to user queries:

• ‘explain’;
• ‘compare’;
• ‘give an example’;
• ‘summarise briefly’.

This involves using human feedback (RLHF), which makes the responses more useful and structured.

How an LLM generates text

Despite the complexity of its behaviour, the underlying principle is quite simple: the model predicts the next token.

A token is a part of a word, a character, or a whole word. At each step, the LLM evaluates the probabilities of how the text might continue and selects the most appropriate option.

This is precisely why the same query can yield different formulations of the response.

Open and closed LLMs: what’s the difference?

Modern language models fall into two categories:

Closed LLMs

Closed models are accessible via the provider’s API. Users benefit from a ready-to-use infrastructure and a quick start.

Advantages:

• high performance;
• ready-made integrations;
• regular updates.

Disadvantages:

• dependence on an external service;
• limited control over data;
• inability to manage the model at the weight level.

Open LLMs

Open models can be deployed within a company’s own infrastructure.

Advantages:

• data control;
• customisation;
• ability to fine-tune;
• operation in an isolated environment.

This approach is particularly important for companies with high security and regulatory compliance requirements.

Popular LLM models

GPT-4 and ChatGPT

Some of the best-known large language models. They are used for text generation, code generation, analytics and document processing.

Their key feature is a well-developed ecosystem of APIs and tools.

LLaMA

A family of open-source models from Meta. Often used in corporate projects due to the possibility of on-premises deployment.

Claude

A model designed for high-quality processing of long documents, legal texts and complex instructions.

DeepSeek

A popular family of open-source models for code generation and corporate AI services.

Qwen

Models from Alibaba Cloud used for multilingual tasks, analytics and business process automation.

BERT

BERT is a text understanding model. It is more commonly used for classification, search and entity extraction, rather than for content generation.

Advantages of LLMs

Versatility

A single model can handle dozens of tasks:

• text generation;
• answering questions;
• coding assistance;
• information retrieval;
• document analysis.

Scalability

LLMs are suitable for both pilot projects and large-scale enterprise systems.

Rapid adaptation

Models can be integrated relatively quickly into company processes by customising prompts and connecting internal data.

Limitations and risks of LLMs

Hallucinations

LLMs may confidently generate inaccurate information.

Security risks

Working with personal and corporate data requires access control, compliance with legal requirements and infrastructure protection.

High resource requirements

Training and running large models require GPUs, high-performance servers and scalable infrastructure.

What is needed to implement an LLM

Infrastructure

To run an LLM, you will need:

• GPU servers;
• high-speed networks;
• scalable storage;
• fault-tolerant infrastructure.

Data

Models deliver the greatest value when working with internal corporate data:

• knowledge bases;
• contracts;
• documentation;
• support tickets.

LLMOps and maintenance

Stable operation requires:

• monitoring;
• response quality control;
• cost management;
• model updates;
• security configuration.

LLMs in corporate infrastructure

For businesses, it is not only the capabilities of the model that matter, but also operational security.

ITGLOBAL.COM provides secure infrastructure for deploying large language models across the Middle East, including the UAE, Saudi Arabia, Qatar, and Oman. 

The company provides:

• AI as a Service (AIaaS);
• infrastructure for neural network inference;
• access to over 50 LLMs;
• support with the implementation and scaling of AI projects.

This approach allows corporate LLMs to be deployed without transferring sensitive data to external services.

LLM Development Trends in 2026

Multimodality

Models work not only with text, but also with:

• images;
• audio;
• video.

Optimisation and Energy Efficiency

Companies are reducing the cost of inference through quantisation and compact architectures.

Integration into business processes

LLMs are becoming part of automated workflows and enterprise services.

Control and verifiability

There is growing demand for:

• auditing of model responses;
• source transparency;
• control over generation;
• query logging.

Conclusion

Large language models have already become an integral part of modern IT infrastructure and corporate services. They help automate text processing, accelerate development, improve information retrieval and support business processes.

However, LLMs are not a universal ‘mind’, but rather a tool that operates on the basis of statistical patterns and text prediction.

Companies that understand how LLMs work and build the right infrastructure around them are gaining a competitive advantage today.