RoPE vs Positional Encoding | Why RoPE Handles Long Context Better

Transformers need positional information to understand word order. Traditionally, they used positional encoding. But modern large language models often use something called RoPE (Rotary Positional Embedding) instead. Why? Because RoPE handles long context more efficiently. In this video, I explain RoPE vs traditional positional encoding in a simple, visual way, with no heavy math. You’ll understand why positional encoding struggles with long sequences, and how RoPE enables better extrapolation and long-context performance. In this video, you’ll learn: Why transformers need positional inf…