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, the encoder reads an input sequence and the decoder generates an output sequence, but both parts need to know the order of tokens because the model itself has no built‑in sense of position. To give each token a sense of its place, the model adds a positional encoding vector—essentially a numeric pattern that changes with each position—to the token embeddings before feeding them to the encoder or decoder. This added signal lets the attention mechanism weigh nearby tokens more strongly and helps the decoder align its output with the correct input positions, which is crucial when business data, like sales forecasts, depend on temporal order. For example, if a retailer wants to predict next‑month sales, the encoder uses positional encodings to understand that “January” precedes “February,” so the decoder can produce a realistic forecast sequence that respects the chronological order. The result is more accurate and coherent predictions in real‑world business tasks.