In the context of Transformer architecture used in business applications, how does the encoder-decoder structure utilize positional encoding to enhance data processing?

In a Transformer, each word or token is first turned into a vector that describes its meaning, but the model itself does not know which token comes first or last because it processes all tokens at once. Positional encoding adds a small, fixed‑size vector to each token vector that tells the model where that token sits in the sequence, so the encoder can learn patterns like “first item” or “last item. ” The decoder receives the encoder’s output, which already contains positional information, and uses it to generate the next token in the correct order, which is crucial for tasks such as translating a business report or forecasting sales. For example, when predicting next month’s sales from a sequence of monthly figures, positional encoding helps the model understand that the most recent month matters more than the earliest month, improving the forecast’s accuracy. Thus, positional encoding lets the encoder-decoder structure respect the order of data, enabling more reliable business insights.