What is an LLM (Large Language Model): Definition, How It Works and Examples

The technology behind ChatGPT, Claude and Gemini

If you have used ChatGPT, Claude or Gemini, you have already interacted with an LLM without knowing it. An LLM (Large Language Model) is the artificial intelligence engine that makes it possible for these tools to hold conversations, write texts, code or answer questions in an almost human way.

In simple terms: an LLM is an artificial neural network trained on massive amounts of text —books, websites, articles, source code— that learns human language patterns and uses that knowledge to generate new responses.

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What exactly is an LLM?

An LLM is a machine learning model designed to understand, interpret and generate human language. Its main characteristics are:

• Massive scale: It contains millions or even billions of adjustable parameters that store learned language knowledge.
• Training with massive text: It is fed with petabytes of text from public sources: books, web pages, academic articles, code, forums and more.
• Autoregressive generation: Every word it generates is based on all preceding words, predicting the most likely one at each step.
• Versatility: A single LLM can translate, summarize, write, explain, code and answer questions without switching tools.

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How does an LLM work internally?

An LLM's operation can be divided into three main phases:

1. Data collection

Huge volumes of text are collected from public sources. This includes websites, books, academic articles, GitHub code, forums and more. All text is cleaned, normalized and prepared for training.

2. Training (pre-training)

The model is trained with an apparently simple task: predicting the next word in a sequence. If we give it "The sky is colo", the model learns that "ur" is the most likely continuation.

This process is repeated billions of times with sentences from across the internet. Over time, the model learns:

• Correct grammar and spelling.
• Semantic relationships: knowing that "king" and "queen" are related.
• Factual knowledge: that Paris is the capital of France.
• Basic reasoning: inferring answers from context.
• Writing styles: distinguishing a formal email from a tweet.

3. Fine-tuning

After pre-training, the model is adjusted with specific dialogue and instruction data to improve response quality. This step is what separates a raw model from a useful chatbot.

The text generation process

When you type something to an LLM, the model:

1. Tokenizes your text, splitting it into fragments called tokens (words or parts of words).
2. Analyzes the full context of all previous tokens.
3. Calculates probabilities for each possible next token.
4. Selects the next token (using techniques like temperature to control creativity).
5. Repeats the process token by token until the response is complete.

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The most well-known LLMs in 2026

Model	Company	Key characteristics
GPT-4o / GPT-5	OpenAI	Powerful, versatile, integrated into ChatGPT and the API
Claude 3.5 / Claude 4	Anthropic	Safety-focused, long responses and reasoning
Gemini 2.0	Google	Natively multimodal, integrated with Search and Android
Llama 3	Meta	Open source, can run locally
Mistral Large	Mistral AI	European, efficient, open source
DeepSeek V3	DeepSeek	Competitive in reasoning and programming

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What are LLMs used for in practice?

LLMs are used today in a wide variety of applications:

• Chatbots and assistants: ChatGPT, Claude, Gemini answer questions and hold conversations.
• AI-powered search: Google SGE and Bing use LLMs to give direct answers instead of just links.
• Automatic translation: Tools like DeepL leverage language models to translate with near-human quality.
• Code generation: GitHub Copilot uses an LLM to suggest and write code in real time.
• Document analysis: Companies process thousands of contracts, reports or emails automatically.
• Education: Platforms that explain concepts adaptively to the student's level.
• Healthcare: Medical assistants that help diagnose or summarize patient records (with human supervision).

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Limitations and hallucinations: what you need to know

Although LLMs are incredibly powerful, they have important limitations every user should know:

• Hallucinations: The model can invent data, names or sources that seem real but are not. This is perhaps the best-known risk.
• Outdated knowledge: Most LLMs have a cutoff date in their training data. They do not know what happened after that date unless given internet access.
• Training biases: If training data contains biases, the model will inherit and perpetuate them.
• Lack of deep logical reasoning: Although it simulates reasoning, it is actually predicting statistical patterns. This can lead to subtle logical errors.
• Prompt dependency: Response quality depends heavily on how you phrase your question. A poor prompt yields a poor result.

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Tips for using LLMs effectively

1. Be specific: Instead of "explain physics," ask "explain special relativity with a 200-word example."
2. Request a specific format: "Give me a 5-point list," "in table format," "in a professional tone."
3. Always verify: Do not blindly trust data, figures or references. Check what matters.
4. Iterate: If the first response is not ideal, adjust your prompt and ask again.
5. Understand the limitations: An LLM is an assistant, not an encyclopedia or an absolute source of truth.

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Verdict

LLMs are the technology that has unlocked the generative AI revolution in 2026. They are incredibly useful for translating, writing, analyzing and creating content, but they are not infallible. The key to making the most of them is understanding what they really are: very powerful statistical tools that simulate human language without genuinely understanding it. Use them as assistants, not as sources of truth, and always put your human judgment at the center.

Want to learn more about the most popular assistants? Read our complete guide on what is ChatGPT or discover the best free AI tools in 2026. If you are interested in the full potential of this technology, check out our beginner's guide to generative AI.