Understand Positional Encoding In Transformers
Last Updated on October 15, 2025 by Editorial Team
Author(s): Ramakrushna Mohapatra
Originally published on Towards AI.
Understand Positional Encoding In Transformers
If you’ve ever worked with Transformer models like BERT, GPT, or T5, you’ve probably come across a small but crucial concept: Positional Encoding.
This article explains the vital role of positional encoding in Transformers, a mechanism that allows models like BERT and GPT to understand word order in a sentence, contrasting with RNNs that handle sequences inherently. It covers how positional encoding injects order information into parallel-processing models, emphasizing the mathematical basis of sinusoidal functions for effectively encoding position. The article demonstrates the unique structure of these encodings, visual representations, and highlights their relevance in maintaining understanding of sequences and enhancing model generalization to longer sequences.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
