Positional encoding is a technique used in neural networks, particularly in models like Transformers, to provide information about the order of input data. Since these models process input sequences simultaneously, they need a way to understand the position of each element within the sequence. Positional encoding adds unique values to each position, allowing the model to differentiate between them. This encoding typically involves using sine and cosine functions to generate a set of values for each position. These values are then combined with the input embeddings, ensuring that the model can capture the sequential relationships and dependencies in the data effectively.