An AI model that can learn and work with this kind of problem needs to handle order in a very flexible way. The old models—LSTMs and RNNs—had word order implicitly built into the models. Processing an input sequence of words meant feeding them into the model in order. A model knew what word went first because that’s the word it saw first. Transformers instead handled sequence order numerically, with every word assigned a number. This is called "positional encoding." So to the model, the sentence “I love AI; I wish AI loved me” looks something like (I 1) (love 2) (AI 3) (; 4) (I 5) (wish 6) (AI 7) (loved 8) (me 9).