Positional encoding adds information about token order to the input embeddings, which is essential since self-attention itself is order-agnostic. The article notes that without it, Transformers couldn't distinguish between different token sequences. Positional encodings are added to embeddings before entering attention layers. They are often based on sine and cosine functions or learned parameters. This step allows the model to incorporate sequence structure. It directly affects how attention layers process and relate tokens. Understanding this encoding explains how Transformers capture order without recurrence.