From probability to dialogue: How the "brain" of an LLM works

What we perceive as a smooth chat is actually an extremely sophisticated form of statistical prediction. Large language models (LLMs) don't work with word meanings the way we do, but with numbers. The fundamental building block is a process called tokenization. Instead of whole words, the model splits text into smaller pieces – tokens. A token can be a whole word, part of a word, or even just a punctuation mark.

When you ask a question, the model doesn't look up the answer in a database like Google. Instead, it tries to estimate: "What token should follow this string of tokens to make sense?" This process happens billions of times per second thanks to an architecture called Transformer, introduced by Google in 2017. It's the attention mechanism that allows the model to understand relationships between words even in distant parts of the sentence, which is key to the AI "knowing" which subject a pronoun refers to three sentences later.

For a deeper understanding of this mechanism, we recommend the video What Makes ChatGPT Chat? Modern AI for the layperson, which very elegantly deconstructs this concept for non-technical users.

RLHF: Why it's not just a random text generator?

If we only took a pure predictive model, we'd get texts that are grammatically correct but often nonsensical or dangerous. What makes ChatGPT a "chatbot" and not just an auto-complete tool is the process of RLHF (Reinforcement Learning from Human Feedback).

In this step, human trainers evaluate the model's responses. If the AI answers in a friendly, factual, and safe manner, it receives a "reward." If it starts hallucinating (making up facts) or being aggressive, the model is penalized. It's precisely thanks to RLHF that models have learned to follow conversational rules: letting the user speak, asking for clarification, and maintaining conversation context. This process is crucial for safety, a topic highly relevant to European regulation under the EU AI Act, which places great emphasis on transparency and safety aspects of high-risk systems.

Comparing market leaders: Who's leading the race?

In 2026, the question is no longer whether AI exists, but which model is best for which task. Here's a current overview of the main players:

• OpenAI (GPT series): Still the gold standard for versatility and ecosystem. Excellent integration into tools like Microsoft Copilot.
• Anthropic (Claude series): Often preferred for its more "human" tone and ability to work with huge volumes of data without losing context. Claude 3.5/4 shows excellent results in logical tasks and programming.
• Google (Gemini): The neural network best integrated into Google Workspace. Its main advantage is native multimodality – the ability to process video, audio, and text simultaneously within a single window.
• Meta (Llama - Open Source): A key player for companies that want control over their data and to run models on their own servers.

When it comes to benchmarks, models in the area of MMLU (Massive Multitask Language Understanding) range between 85–90%, meaning that in expert knowledge tests they reach the level of an expert across a broad spectrum of topics.

Practical impact for Czech users and businesses

What does all this mean for you? For an everyday user in the Czech Republic, it means the entry barrier is minimal. ChatGPT and Claude are both fully available in Czech and handle our grammatical nuances (though more complex cases can still occasionally confuse them).

For Czech businesses:

• Costs: A subscription for individuals (ChatGPT Plus) is around 20 USD (approx. 470 CZK) per month. For businesses, API interfaces are available with pay-per-use pricing (tokens), making it possible to build custom customer assistants.
• Localization: Although the models speak Czech, human review is still necessary for professional use in Czech legal or medical environments. AI understands the language but may not fully grasp the specifics of the Czech legislative context.
• Security: Due to the strict EU AI Act, Czech businesses implementing AI must ensure that data is not used to train public models without consent, which enterprise versions address.