Positional encoding is a technique that helps AI models understand the order of words or elements in a sequence. Unlike traditional models, transformers lack inherent sequence awareness, so positional encoding provides each token with information about its position within the input.
Positional encoding adds unique signals to each token’s data to mark its position. These signals combine with token data before processing, enabling the AI to differentiate between, for example, the first and last words. This allows models to interpret sequences and contexts accurately without relying on recurrence or convolution.
For AI product managers, positional encoding improves model accuracy in language tasks, enhancing user experience and output quality. It supports scalability by enabling efficient transformer architectures, reducing latency and computational costs compared to older sequence models, thus enabling more feasible and cost-effective deployment.