Behind tools like ChatGPT, Gemini and Claude is the same idea: a very large model that has learned to predict the next word.
The basic idea
A large language model is an artificial neural network trained on a very large amount of text. Its job during training is simple: predict which word comes next in a piece of text.
Doing this billions of times, it learns the rules of language, the relationships between topics and common patterns of knowledge. When you ask it a question it does the same thing: it produces the most likely continuation of your question, word by word.
What does it do well?
- Summarizing, rewriting and translating text
- Turning messy notes into an organized draft
- Writing and explaining code
- Explaining a topic at different levels
Where does it go wrong?
The model does not look things up in a knowledge base; it produces text that looks likely. That is why it can state wrong information confidently. It does not know anything after its training data ends and cannot reach your company-specific information on its own.
Once you know these limits, the tool is much safer to use: verifying important facts and giving the model the context it needs solves most problems.