A real Large Language Model (LLM) is incredibly complex, but this simulation uses a simple rule-based approach to show the core idea: predicting the next word.
- Input as Tokens: Your prompt is broken down into words, which we can think of as "tokens". The model looks at the last token to decide what to write next.
- Finding a Probable Next Word: Based on the current word, the model looks up a list of possible words that could follow. In this simple demo, it just picks one randomly from a pre-defined list. A real LLM calculates probabilities for thousands of possible next words based on patterns it learned from massive amounts of text data.
- Appending and Repeating: The chosen word is added to the sequence. It then becomes the new "current word", and the process repeats, generating text one token at a time.
This step-by-step generation is why you sometimes see models typing out their answers, as they are actively "thinking" of the next best word to add to the sentence.