What is an LLM? Understand everything in 3 min

You've read “LLM” ten times this week without really knowing what that means. Do not panic: in 3 minutes, you will understand what is hidden behind these three letters that transform all jobs.
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LLM: simple definition of a Large Language Model

An LLM, for Large Language Model, is an artificial intelligence program trained to understand and generate text. In French, we talk about big language models. Concretely, an LLM is able to read a question, to grasp its meaning, and to formulate a coherent answer. Exactly like a human would. Except it does it in seconds and on a large scale.

These language models are based on deep learning, an advanced branch of machine learning. They have been trained on billions of pages of text: books, articles, websites, forums, technical documentation. The result? A system capable of text generation, text analysis, machine translation, summarization, and many other natural language processing tasks.

Natural language processing, or NLP (Natural Language Processing), is the field of artificial intelligence that deals with making machines understand human language. LLMs are the latest generation. They were not programmed with manual grammatical rules. They learned to write by looking at huge amounts of text. That's what makes them so versatile and so amazing.

To remember : An LLM is a broad language model, that is, a large language model based on deep learning. It is capable of understanding, analyzing, and generating text in virtually any context.
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How does an LLM work? Explanation without jargon

Understanding how an LLM works does not require a doctorate in computer science. You just need to grasp three fundamental building blocks: the architecture that makes it run, the way it cuts text, and the training method that makes it efficient.

Transformer architecture: the driving force behind LLMs

It all started in 2017, when Google researchers published an article that became legendary: “Attention is All You Need”. In it, they present the transformer architecture, a new type of neural network designed specifically for natural language processing.

Before transformers, language models read text word by word, in order. It was slow and not very effective in understanding the context of a long sentence. Transformer architecture has changed the game thanks to a key concept: the attention mechanism.

The attention mechanism allows the model to analyze all the words in a sentence in parallel. Even better, thanks to self-attention, each word “looks” at every other word to understand its own role in the sentence. Take the phrase “The river bank was steep.” The word “bank” does not have the same meaning as in “I opened a bank account.” It is self-attention that allows the model to make this distinction by analyzing the words around.

This ability revolutionized NLP. All the major current language models, whether GPT, Claude, Gemini or Mistral, are based on this transformer architecture. Without it, none of the artificial intelligence tools you use today would not exist in this form.

Tokenization, pre-training and fine-tuning: the three key steps

An LLM doesn't read words. It reads tokens. Tokenization is the process of breaking up plain text into small units that the model can process. For example, the word “unbelievably” could be split into “incredible” and “lying.” This step is critical: it determines how the model understands and produces text.

Once tokenization is in place, the training of an LLM is done in several phases. Here are the top three:

• Pre-workout : the model ingests massive volumes of training data. It learns to predict the next word in a sentence over and over again, out of billions of examples. This is the most computationally expensive phase. It gives the LLM its general knowledge and its mastery of the language. Machine learning does the heavy lifting here.
  
  
• Fine tuning : once the pre-training is over, the model is refined to more specific data. For example, you can specialize an LLM in code generation, in the legal field or in customer service. Fine tuning makes the model more accurate for a given use without starting from scratch.
  
  
• Human alignment : it is the most recent and the most strategic step. Using a technique called RLHF (Reinforcement Learning from Human Feedback), humans evaluate the model's responses and teach it how to be more useful, honest, and less dangerous. Human alignment is what makes the difference between a raw LLM and a reliable assistant like the ones used in business.
  
  

Each of these steps is based on huge volumes of training data. The quality and diversity of these data have a direct impact on the performance of the final model.
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What are LLMs for? Use cases

There is a lot of talk about theory. But what most professionals are interested in is the actual use. So, in concrete terms, what are the use cases of LLMs today?

Chatbots and chatbots are the most visible application. Millions of people talk to ChatGPT, Claude, or Gemini every day to get answers, write emails, prepare for meetings, or explore ideas. In business, chatbots take care of customer support, lead qualification, and even the onboarding of new employees.

Text generation is another massive field. LLMs write articles, marketing briefs, LinkedIn posts, product descriptions, meeting minutes. A marketing manager who spent half a day writing a newsletter can now produce a first draft in a few minutes. The quality isn't always perfect the first time, but the productivity gain is real.

Code generation is transforming the daily lives of developers. Tools like GitHub Copilot, powered by LLMs, suggest code in real time, detect bugs, and offer refactoring. Even non-technical profiles are starting to create scripts and automations thanks to large language models.

Machine translation has taken a dramatic leap forward. LLMs no longer translate word for word. They understand the context, cultural nuances, and language register. This is a huge asset for businesses that operate internationally and need fast and reliable location.

Text analysis makes it possible to extract insights from large quantities of documents. Legal contracts, customer feedback, financial reports, internal surveys: LLMs know how to summarize, classify, identify trends and get the most out of thousands of pages in a few seconds.

And those are just the most common uses. We also see use cases of LLMs in scientific research, the creation of educational content, the automation of HR tasks, competitive intelligence and the synthesis of strategic information.
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LLM open-source vs proprietary models: the current landscape

When it comes to LLM, two main families stand out. On the one hand, proprietary models. On the other hand, open-source LLMs. Understanding this distinction is critical to making an informed choice.

The proprietary models that dominate the market

OpenAI GPTs are the best known. They power ChatGPT and thousands of applications via API. Claude, developed by Anthropic, is distinguished by its reliability and its ability to process long documents. Gemini, from Google DeepMind, focuses on integration into the Google ecosystem. These proprietary models offer top performance. But they involve a cost of use, a dependency on a supplier, and an opacity about the training data used.

Open-source LLMs that are gaining momentum

Faced with the giants, an alternative is being structured. LLama from Meta, Mistral from the French startup Mistral AI, or even Falcon and Qwen offer language models that are open, free and editable. The benefits of the open-source LLM are threefold: total transparency on how the model works, the possibility of fine-tuning on one's own data, and data sovereignty since everything can run on its own servers.

To choose between open-source LLM and proprietary models, four criteria count. First, the cost: proprietary models charge per use, open-source LLMs require infrastructure. Customization then: fine-tuning is much more accessible with open-source. Raw performance: proprietary models often stay ahead of the curve, even if the gap is rapidly closing. And finally, confidentiality: if your data is sensitive, an open-source LLM deployed internally is often more careful.
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Advantages and limitations of LLMs: what you need to know before starting

No technology is perfect. Large language models are no exception. Having a clear vision of the benefits of LLMs and their limitations is essential to use them intelligently.

What LLMs do remarkably well

The first benefit of LLMs, it's the gain in productivity. Any task that involves text generation, text analysis, or information synthesis is now done in a fraction of the time. A lawyer who analyzes an 80-page contract, a marketer who writes 10 variants of an email, a developer who debugs a complex function: they all save hours every week.

Versatility is another major advantage. A single LLM can handle dozens of different NLP tasks without the need for a specialized model for each one. Text generation, machine translation, classification, entity extraction, summary, code generation: everything goes through the same system.

Accessibility is progressing as fast as technology. APIs are simple to integrate, no-code interfaces are multiplying, and open-source LLMs allow modest teams to deploy artificial intelligence solutions without an astronomical budget. Fine tuning makes these models adaptable to almost all sectors and professions.

AI hallucinations, biases, and other gray areas

Let's talk about the things that are annoying. AI hallucinations are LLM's Achilles heel. Sometimes a great language model invents facts, cites non-existent sources, or gives a completely wrong answer with disconcerting aplomb. It's not a one-time bug. It's a structural characteristic of how these models generate text. They predict probable sequences, not truths. Checking the outputs of an LLM therefore remains essential, especially in high-stakes contexts.

LLM biases are another critical topic. Training data inevitably contains biases: social, cultural, gender, geographic. The model absorbs them and reproduces them in its responses. Human alignment reduces the problem, but does not completely eliminate it. LLM biases are a permanent challenge for all artificial intelligence laboratories.

There is also the energy and environmental cost. Training an LLM consumes a massive amount of energy and water. The limits of LLMs are not only technical: they are also ecological and ethical. And finally, opacity: no one really knows why an LLM produces one response over another. This “black box” problem raises serious questions about responsibility, especially in health, justice or finance.
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What LLMs will change in the next 12 months

We are still at the beginning. As impressive as the big language models are today, what's happening is going to speed things up.

The first strong trend is the emergence of autonomous AI agents. Today, chatbots answer questions. Tomorrow, conversational agents will perform complex tasks independently. Take a brief, research data, write a report, send it by email and schedule a follow-up. Without human intervention between each stage. We are moving from the passive language model to active artificial intelligence.

The specialization of language models by sector will also accelerate. LLMs are already emerging tailored for health, legal, finance or education. Fine-tuning on specific business data produces results that are much better than those of a generalist model. This trend will make big language models accessible and relevant for businesses that thought artificial intelligence was not for them.

The democratization of fine-tuning for SMEs is another fundamental movement. Until recently, honing an LLM required rare skills and large budgets. The tools are being simplified. The costs are going down. Companies of 20 people will be able to deploy a specialized language model on their own data, hosted on their infrastructure, without depending on an American or Chinese actor.

The challenge of human alignment will also increase with regulation. The European AI Act is gradually coming into force. It imposes standards of transparency, security and responsibility on providers of artificial intelligence models. Companies that integrate LLMs into their processes must be able to justify the reliability of their systems. Human alignment is moving from nice-to-have to must-have.

Finally, the convergence between LLM and business tools will transform workflows. CRMs, ERPs, marketing tools, project management platforms will natively embed language models. We will no longer talk about using a chatbot next to its tool. The model will be in the tool, invisible and efficient.
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And now, how do we take action?

You now know what an LLM is, how it works, what it is for and what its strengths and limitations are. The real question is no longer “what is a broad language model”, but “how do I integrate it into my business to get a concrete benefit from it”.

That's exactly what we do at Scroll. We support companies in the operational integration of artificial intelligence: audit of use cases, choice of the right language model, deployment, team training. No slides, no vague promises. Concrete, tailor-made, and measurable results.

Do you want to know what an LLM can do for your business? Let's talk.