Positional encoding injects word-order information into token embeddings, since self-attention on its own treats input as an unordered set of tokens.

Without it, “dog bites man” and “man bites dog” would look identical to the model. Common approaches include sinusoidal encodings (original Transformer), learned position embeddings, and rotary position embeddings (RoPE) used in most modern LLMs.