What Is a Large Language Model (LLM) and Why It’s Changing Everything

A Large Language Model (LLM) is an AI model trained on massive amounts of text to predict the most likely sequence of words given a context. GPT-4, Claude, Gemini, and Llama are LLMs. When you type a prompt into ChatGPT, there’s an LLM behind the scenes processing your request. They’re reshaping customer service, software development, and marketing — and creating new careers, from Prompt Engineer to AI Product Manager, that are among the most in-demand in 2025.

When you asked ChatGPT to write you an email, you used a Large Language Model. When GitHub Copilot completed a line of code for you, same thing. And when a company replies via chat in a way that feels like a real person, more and more often it’s an LLM. Yet almost nobody knows what “Large Language Model” actually means, how it works, or why it’s disrupting entire industries. At H-FARM College we see LLMs as one of the key tools of future work, and training students to build real products on top of them is part of our DNA. In this article you’ll understand what an LLM is, how it works, the main ones today, and which careers use them every day.

What Is a Large Language Model: A Plain-Language Definition

A Large Language Model is an AI system that has read enormous amounts of text (books, articles, code, conversations) and learned to predict which word comes next in a given context. From this simple capability — predicting the next word — complex behaviors emerge: answering questions, writing code, translating languages, summarizing documents.

“Large,” “Language,” “Model”: What Each Word Actually Means

Large: the model has billions (in some cases trillions) of parameters. GPT-3 had 175 billion, GPT-4 likely has over 1 trillion (OpenAI hasn’t confirmed the exact figure).
Language: it works with natural language, not just programming or numbers.
Model: it’s a deep neural network with transformer architecture.

The numbers are impressive, but the most surprising thing is that from an apparently simple mechanism — “predict the next word” — emerge capabilities that look like reasoning. Although “reasoning” is a word to handle carefully, as we’ll see.

How an LLM Works: Tokenization, Transformers, and Probability

Everything starts with tokenization: text is broken into smaller units (tokens), which can be whole words, syllables, or single characters. The word “intelligence” might become “intel” + “ligence” — two tokens.

What Really Happens When You Type a Prompt into ChatGPT

When you type “What is two plus two?” here’s what happens, simplified:

The prompt gets tokenized into numerical chunks
Those numbers pass through dozens of layers of a transformer
At each layer, the network calculates which token is most likely as the next word
The highest-probability token is “extracted” and added to the response
The process repeats until a full response is generated

It all happens in milliseconds. Nothing “magical” — just math at huge scale. Building a system that integrates LLMs into a real product requires both front-end and back-end skills: to understand the technical profile best suited to this kind of work, read our article on what a Full Stack Developer does and why they are among the most in-demand profiles in tech.

Pre-training, Fine-tuning and RLHF: The Three Phases of an LLM

Building an LLM requires three phases:

Pre-training: the network is trained on massive amounts of text to predict the next word. Costs tens of millions of dollars in compute. The “heaviest” phase.
Fine-tuning: the model is specialized on specific tasks or domains (medical, legal, customer support).
RLHF (Reinforcement Learning from Human Feedback): humans give feedback on good vs bad answers, and the model learns what they prefer. This phase turned GPT-3 (powerful but awkward) into ChatGPT (useful and friendly).

The Main LLMs Today: GPT-4, Claude, Gemini, Llama

The 2025 LLM landscape has four main families:

GPT-4 / GPT-4o (OpenAI): the model behind ChatGPT, market leader
Claude (Anthropic): known for accurate reasoning and structured outputs
Gemini (Google): integrated into Google Workspace, strong on multimodality
Llama (Meta): the main open-source family, downloadable and fine-tuning-friendly

Open Source vs Closed Source: The Differences That Matter

Closed-source models (GPT-4, Claude, Gemini) are accessible only via API: you pay per use and don’t see the code. Often more powerful but tie you to one vendor.

Open-source models (Llama, Mistral, Qwen) you can download, modify, fine-tune on your data, and run on your own servers. They’re the choice for those with privacy constraints (healthcare, legal) or who want independence.

LLM Applications in Business and Work

LLMs are moving into every business process. According to McKinsey (2024), 65% of companies already use generative AI in at least one function, up from 33% the previous year.

Customer Service, Coding Assistants, Document Analysis, RAG

The most common applications today:

Customer service: advanced chatbots that resolve first-tier tickets (Intercom Fin, Zendesk AI)
Coding: GitHub Copilot and Cursor accelerate software development by 30–55% according to GitHub studies
Document analysis: automated summaries of contracts, financial reports, legal documents
RAG (Retrieval-Augmented Generation): the LLM is “plugged into” an internal documentation base and answers by citing internal documents

A particularly interesting use case is marketing: LLMs automatically generate personalized emails, A/B variants of landing pages, and content for every stage of the marketing funnel — the path that guides a user from brand discovery to purchase. Understanding how a funnel is built is today a key skill even for those working with AI.

LLM Limitations: Hallucinations, Bias, and Context Windows

LLMs have significant limitations that anyone working with them needs to know:

Hallucinations: they produce false but plausible statements. An LLM can fabricate a quote, a ruling, an author name. Always verify.
Bias: they inherit biases present in training data.
Limited context: every LLM has a maximum “context window” beyond which it forgets.
Knowledge cutoff: they only know what was in the training data at training time. They don’t know what happened after.

Careers That Work With LLMs Every Day

LLMs are creating new hybrid roles. Among the most in-demand:

Prompt Engineer: designs effective instructions to get the best out of LLMs
AI Product Manager: manages LLM-based products, balancing capabilities and limits
LLM Engineer: integrates LLMs into enterprise software, manages fine-tuning and RAG
AI Content Strategist: orchestrates hybrid human-LLM editorial workflows
AI Ethics Officer: handles accountability, bias, copyright

In Italy, a mid-level AI Product Manager earns between €45,000 and €65,000 gross per year (LinkedIn 2025 data), with peaks of €90,000–€110,000 in tech multinationals. A senior LLM Engineer at international companies can easily exceed €100,000.

How H-FARM College Prepares You for the LLM Era

At H-FARM College we believe LLMs are truly understood only by building products with them. That’s why at our Roncade campus you’ll work with OpenAI, Anthropic, and Google APIs from your first semesters, on real challenges brought in by partner companies like Microsoft. Two programs prepare you in concrete ways:

The AI for Business Transformation program trains you to manage AI projects in companies, integrating LLMs into real products and workflows.
The Bachelor’s Degree in AI & Data Science is the most technical track, focused on design, fine-tuning, and deployment of language models.

You’ll have a faculty of industry experts, access to cloud and GPU infrastructure, and an alumni network already working at companies building the future of AI. Want to see life on our campus? Book the next Open Day or contact us to meet the H-FARM team.

FAQ

Is ChatGPT a Large Language Model?

Yes. ChatGPT is built on GPT-4, an LLM developed by OpenAI. Claude (Anthropic), Gemini (Google) and Llama (Meta) are also Large Language Models — all trained on vast amounts of text to generate coherent, contextual responses.

Does an LLM actually understand what it’s saying?

Not in the human sense. An LLM predicts the most likely sequence of words given a context. It doesn’t “understand” — but it’s so good at prediction that the output often looks like real comprehension. This is one of the great debates in modern AI.

How much does it cost to train an LLM?

Training a model like GPT-4 costs tens of millions of dollars in compute. That’s why most companies use pre-trained LLMs via APIs and adapt them with fine-tuning on proprietary data — far more cost-effective and accessible.

How do companies use LLMs today?

Companies use LLMs via APIs (OpenAI, Anthropic, Google) to automate content generation, build advanced chatbots, analyze documents, translate text, and support development teams with code assistants like GitHub Copilot or Cursor.

What skills do you need to work with LLMs?

Prompt engineering, Python, API knowledge, and NLP fundamentals. Those in product or business roles need to understand LLM capabilities and limitations to design effective AI products. Programs like AI & Data Science at H-FARM College prepare you for these roles.