LLM: a complete guide
to Large Language Models

A model that understands,
processes and generates language

An LLM (Large Language Model) is an artificial intelligence model designed to understand, process and generate natural language. These systems represent one of the most important innovations in the field of artificial intelligence and underpin many modern applications: chatbots, virtual assistants, automated writing tools, language translation and code generation.

Large Language Models are trained on enormous quantities of text data from books, articles, websites, technical documentation and other sources, allowing them to learn linguistic structures, concepts and relationships between words.

Deep neural networks
and the Transformer architecture

LLMs use deep neural networks based on the Transformer architecture, introduced in 2017 and now the foundation of modern generative AI. The process can be simplified into three phases:

1. Training

During this phase the model analyses enormous quantities of text and learns:

• Grammar
• Syntax
• Context
• Semantic relationships
• Linguistic structures

2. Prompt comprehension

When the user submits a request, the model interprets the meaning of the text and identifies the context.

3. Response generation

The LLM statistically predicts which words are most likely to follow the previous ones, building a coherent response.

Predefined rules
vs generative flexibility

Traditional AI

Traditional systems follow predefined rules and are generally designed for specific tasks. Examples:

• Expert systems
• Classification algorithms
• Recommendation engines

Large Language Models

LLMs are more flexible and can handle many different tasks:

• Text writing
• Translation
• Summarisation
• Document analysis
• Programming
• Customer support
• Information retrieval

Why they have become
a strategic tool

Greater productivity

Many tasks that used to take hours can now be completed in a matter of minutes.

Process automation

LLMs make it possible to reduce manual work across numerous sectors.

Accessibility of information

Information can be processed and summarised rapidly.

Scalability

A single model can assist thousands of users simultaneously.

Two categories,
two strategies

Proprietary models

These are developed by private companies and are generally accessible via API or dedicated platforms.

Characteristics:

• High performance
• Professional support
• Continuous updates

Open source models

These are publicly available and can be installed on private infrastructure.

Advantages:

• Greater control
• Advanced customisation
• Reduced dependence on external vendors

Where
they are transforming sectors

Large Language Models are transforming numerous sectors:

• Digital marketing
• Finance
• Healthcare
• Training and education
• Software development
• E-commerce
• Customer service
• Business consulting

Rather than replacing people entirely, LLMs tend to increase productivity and support professionals in repetitive or high-volume tasks.

Conclusion

Large Language Models represent one of the most significant innovations of the digital age. Through their ability to understand and generate natural language, they are revolutionising the way people and businesses work, communicate and access information.

As generative artificial intelligence continues to evolve, LLMs will keep growing more powerful, opening up new opportunities in automation, productivity and digital transformation.

Frequently asked
questions

What is the difference between an LLM and traditional AI?

Traditional AI (expert systems, classifiers, recommendation engines) follows predefined rules for specific tasks. An LLM learns statistical patterns from enormous quantities of text and can handle very different tasks without being reprogrammed for each one. What it takes to build them is also different: data and compute, not hand-written rules.

Can I run an LLM on my own server without paying a vendor?

Yes, with open source models (Llama, Mistral, Qwen, etc.). The advantage is total control over your data; the downside is that open model quality is still below that of leading proprietary models, and costly GPUs are required. For SMEs this is rarely the first choice; it makes sense when data is highly sensitive or volumes are very high.

How large is an LLM compared to ordinary software?

The smallest usable models weigh a few GB; leading models run to hundreds of GB. A traditional web application, by comparison, weighs a few MB. This is why LLMs do not run natively on smartphones or low-end laptops, and execution requires dedicated infrastructure.

Do LLMs have memory? Do they remember previous conversations?

No, not by default. Every request is independent: the model "sees" only what you send it at that moment. When a chat appears to "remember", the interface is actually resending the entire conversation history with each new message. The "persistent memory" features available today are add-ons built on top of the model, not intrinsic capabilities.

Why can an LLM "invent" facts with confidence?

Because it does not distinguish between what it knows and what it generates plausibly. Statistically, the sentence "the revenue of X in 2023 was Y" sounds valid even when Y is made up. This is why the winning use cases are those where the output is verifiable at a glance, or where you supply the reference data yourself in the prompt rather than relying on the model's "memory".