The system iteratively (step-by-step) generates new vectors representing the next predicted output tokens. These vectors are then decoded back into human-readable tokens, and those tokens are assembled into a coherent, readable text, forming the AI’s final response. This process continues, often token by token, until a complete answer is generated.
In our practical example, after the neural network has identified the vectors for the core concepts (like ‘diplomacy’, ‘peaceful’, and ‘resolution’), it adds a few other vectors for grammatical words – like ‘is’ and ‘of – to construct a complete and readable sentence. These final vectors are converted back into tokens and then into the words that form the answer.
Vectors are converted into tokens (Grok LLM)
and then into the words:
Key takeaways
- AI does not ‘think’ – it predicts the most statistically likely response.
- AI does not ‘understand’ words – it processes mathematical representations of text.