How the language model works
LLM is trained on a huge amount of text to predict the next word. From this simple principle comes the ability to answer, write and reason. The model does not “understand” like a person - it finds a plausible continuation based on learned patterns.
Why the model is sure to be wrong
Because the model generates plausibility rather than testing truth, it can make up a fact, a reference, or a figure—and sound confident while doing so. This is called a hallucination. Therefore, always check important facts.
Tokens, temperature, knowledge frontier
The model works with tokens (pieces of text) - the cost and limits depend on this. Temperature controls creativity: lower is more stable, higher is more varied. And the model has a knowledge boundary - a date after which it does not know about the world; fresh requires an external source.
What does this give in practice?
Knowing this, you use the model smarter: you clearly set the task, check the facts, understand when you need to search for fresh data, and are not surprised by errors. Understanding the tool makes the difference between those who benefit from those who are disappointed.