What is a large language model (LLM)? The definition

A large language model, or LLM for short, is an AI model trained on enormous amounts of text, which is what allows it to understand and generate human language. It is the type of model behind tools like ChatGPT, Google Gemini and Microsoft Copilot. This article gives the broad definition: what an LLM is, how big they are and where they fit in the AI landscape. If you want to know exactly how an LLM arrives at an answer, read our explanation of LLMs for marketers, which goes deeper into how they work.

What is a large language model exactly?

A large language model is an AI system that has learned language patterns from a giant collection of text and uses that knowledge to produce new text. The three words sum it up:

• Large refers to the scale, both the amount of training text and the size of the model itself.
• Language means it works with language, so with words and sentences, not with numbers in a database.
• Model means it is a mathematical system that has learned patterns and applies them to new questions.

According to IBM, an LLM is at its core a model trained to predict, based on context, the most likely next piece of text. It does not contain a ready-made archive of facts that it looks things up in: it rebuilds every answer on the spot.

How “large” is large?

Scale is what sets an LLM apart from smaller language models. Size is expressed in parameters: the internal numbers the model adjusts during training. GPT-3, the model that triggered the breakthrough, counted 175 billion parameters, as stated in OpenAI’s original research, “Language Models are Few-Shot Learners”. Modern models sit in the same order of magnitude or higher.

More parameters roughly means more capacity to store nuance and patterns, but also more computing power and cost. Not every language model needs to be big: a small language model is deliberately more compact, so it can run cheaper and locally.

Where does an LLM fit in the AI landscape?

Let’s put the terms in order, because they are easily mixed up:

• Artificial intelligence is the broad umbrella.
• Machine learning is the branch that learns from data.
• Deep learning is machine learning with a neural network with many layers.
• A foundation model is a broadly trained base model you can deploy for many tasks.
• A large language model is a foundation model specialised in text.

In short: an LLM is a specific application of deep learning to language, built as a foundation model.

The table below places the concepts side by side, from broad to specific.

Why an LLM matters to you as a B2B marketer

Your customers increasingly ask an AI a question instead of scanning ten blue links. The answers they get come from LLMs. That changes two things for your marketing.

First, an LLM is a tool: you use it to create concepts, emails and analyses faster. Second, and more importantly, an LLM is a new search channel. When a model generates an answer, it chooses which sources it mentions or cites. If you want to appear there, it comes down to generative engine optimization: content that is clear, trustworthy and well structured, as an extension of strong SEO. Because an LLM learns from text and patterns, that is exactly what it rewards. That is what we focus on with our GEO agency approach.

A concrete B2B example

Take a software company that sells to HR managers. In the past, a prospect’s search started at Google. Now that same manager types into ChatGPT: “which tools help with remote onboarding?” The LLM composes its answer on the spot and names a few providers. Whether your company is among them depends on how consistently and verifiably you explain online what you solve and for whom. A page that clearly names the problem, the audience and the result gives the model something to hold on to. Vague marketing language does not. In practice we see that the same structure that convinces people also helps an LLM link your name correctly to the question.

Common mistakes around LLMs

These misconceptions cost B2B teams time and trust.

• Treating an LLM as a source of facts. The model predicts text, it verifies nothing. Always have a human check the facts and the sources before anything goes out.
• Thinking bigger is always better. For many tasks a small language model that runs cheaper and locally is enough. The task and the privacy requirements determine the choice, not the number of parameters.
• Confusing volume with value. Generating ten times more content that nobody reads does not help. Steer on pieces that produce qualified leads and that a model can pick up as trustworthy.

Honestly: where is the limit for B2B?

We are clear about it: an LLM does not understand anything the way a human does. It calculates with probabilities, and that is why it can give a wrong answer convincingly. For B2B, where trust and accuracy weigh heavily, that means: use AI as an accelerator, not as an editor-in-chief. Always let a human guard the facts and the tone.

And here too our common thread applies: steer on customers and revenue, not on the newest tool because everyone is talking about it. Deploying an LLM to produce ten times more content that nobody reads is waste. As a small team that moves fast, we choose what delivers qualified leads.

Frequently asked questions

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