Using ChatGPT, Gemini, or Claude has become as everyday as doing a Google search. But understanding what's behind those answers, how a machine "understands" what you ask it and responds in natural language, remains a mystery for most of those who use them every day. This guide is designed so that you can understand what a Large Language Model (LLM) is without needing to know how to program or have technical training. Because in the age of AI, understanding the tools you use gives you a real advantage: both to use them better and to decide which one to choose depending on your work or project.

What exactly an LLM is (with analogies, no jargon)

An LLM (Large Language Model) is an artificial intelligence system trained to process and generate text. It works by predicting, based on patterns learned from enormous amounts of text, what the most probable next word —or "token"— is in a given sequence.

The most useful analogy: imagine an obsessive reader who has read billions of texts —books, articles, forums, code, conversations— and learned to recognize the patterns of language so well that they can continue any sentence coherently. It doesn't "understand" in the human sense, but it predicts with such high statistical precision that it seems to understand.

Four key concepts explained with everyday examples

• Token: the basic unit of text. It can be a complete word, part of a word, or a punctuation mark. LLMs don't "read" letters, they read tokens. The phrase "artificial intelligence" is two tokens; "ChatGPT" can be one or two depending on the model.

• Embedding: a mathematical representation of the meaning of words or phrases in a multidimensional space. It allows the model to "know" that "king" and "queen" are related, and that "bank" can mean a financial institution or a riverbank depending on the context.

• Pretraining: the initial phase where the model learns language patterns from billions of texts. It is the most expensive and lengthy process; it can last weeks or months and requires enormous clusters of GPUs.

• Fine-tuning: subsequent adjustment of the base model on specific data to make it more useful or safe in a concrete use case. This is how a generic model is transformed into a code assistant, a medical writer, or a customer service chatbot.

How an LLM is trained: the simplified process

The training of an LLM occurs in several sequential phases that go from data collection to public deployment:

1. Data collection: trillions of text tokens are gathered from the internet, digitized books, code, and other sources. GPT-4 was trained on more than a trillion tokens of diverse text.

2. Pretraining: the model learns to predict the next word billions of times. Each incorrect prediction adjusts the internal weights of the neural network (through a process called backpropagation).

3. RLHF (Reinforcement Learning from Human Feedback): humans evaluate the model's responses and those evaluations are used to fine-tune the behavior toward more useful, accurate, and safe responses. It is the key that transforms a model that "completes text" into an assistant that "helps".

4. Deployment: the trained model is optimized to run on production hardware and is exposed to users via API or web interface like the one you use in ChatGPT or Claude.ai.

According to research from Hugging Face and its Chatbot Arena Leaderboard, the most used benchmark to compare models, the quality of the training data is as important as the size of the model itself.

The most used models: key differences

Not all LLMs are the same. Each one has different strengths depending on its architecture, training data, and fine-tuning process. These are the most relevant differences among the models you probably already use:

• GPT-4o (OpenAI): very strong in general reasoning, code, and multimodal tasks (text, image, voice). Ideal for complex tasks that require combining different types of information.

• Gemini Pro (Google): native integration with the Google ecosystem (Drive, Gmail, Docs). An advantage in searches with real-time information and tasks that require access to up-to-date data.

• Claude Sonnet (Anthropic): especially good at analyzing long documents, quality writing, and extended reasoning. Context window of up to 200K tokens. A focus on safety and transparency.

• Llama (Meta): an open source model that can run locally without sending data to external servers. Ideal for companies with strict privacy requirements or technical teams that want to customize the model.

How to choose the best LLM for your use case

Choosing the right model depends on three factors: the task you want to perform, the context of use (privacy, integration with other tools), and the available budget. A quick guide:

• For creative writing, document analysis, and information synthesis → Claude

• For code, debugging, and complex technical tasks → GPT-4o or Claude

• For searches with up-to-date information and tasks within the Google ecosystem → Gemini

• For local use without connectivity or with strict privacy requirements → Llama

The most important thing: no model is superior in everything. The most efficient teams usually use more than one depending on the task. TechCrunch publishes up-to-date model comparisons that can help you stay current with market changes.

If you're interested in exploring this topic further, you can also read how to use AI to boost your professional profile.

Recommended Coderhouse courses

If you want to go from user to someone who understands and leverages AI in depth, these Coderhouse courses are the ideal starting point:

• Introduction to Artificial Intelligence Course: key concepts, practical tools, and real use cases. Designed for people without technical training who want to understand and apply AI in their work.

• AI Engineering Course: for those who want to work directly with LLMs and APIs to build their own applications. Includes integrating models like GPT and Claude into real projects.

• AI Automation Course: learn to automate workflows using LLMs as the engine. Ideal for professionals who want to eliminate repetitive tasks without knowing how to program from scratch.

Frequently asked questions

Does an LLM really "think" or "understand"?

Not in the philosophical or human sense. An LLM processes statistical patterns of language with extremely high complexity, which produces responses that seem understanding. But internally it is probabilistic prediction, not cognition. This distinction matters because it explains both the capabilities and the limitations of the model: it can "hallucinate" information because it generates the most probable answer, not the most truthful.

What is the difference between an LLM and a traditional chatbot?

Traditional chatbots follow predefined decision trees: if the user says X, the chatbot responds Y. An LLM generates responses dynamically from learned patterns, so it can handle questions it never "saw" before and hold open conversations without needing preconfigured scripts.

Do LLMs have access to the internet?

It depends on the model and the configuration. Base models do not have internet access: they only know what they learned during training (which has a cutoff date). Some models (like Gemini or ChatGPT with browsing enabled) can search for information in real time, which makes them more useful for questions about recent events.

What is a "context window" and why does it matter?

It is the maximum amount of text the model can process in a single conversation or query. A large window (like Claude's 200K tokens) makes it possible to analyze entire documents, hold long conversations without losing the thread, or process extensive transcripts without splitting them. It is one of the most important factors when choosing a model for long document analysis or complex projects.

Will LLMs replace programmers?

The evidence so far indicates that LLMs significantly boost programmers' productivity (GitHub Copilot studies show increases of up to 55% in speed), but they do not replace the judgment, systems architecture, or complex problem-solving that require business context. The most in-demand profile in today's tech market is precisely that of someone who knows how to program and knows how to work with LLMs.