Understanding Positional Embeddings in Transformers (with Intuition and Examples)

Transformers have become the backbone of modern AI. They power the large language models we interact with daily and are even used in scientific problems like protein structure prediction. But there’s a subtle issue with them. Unlike older models such as RNNs, transformers don’t naturally understand sequence order. Instead of reading a sentence word-by-word, they process all tokens in parallel. That means the model initially sees a sentence like a set of words rather than an ordered sequence.