Giving the Model a Sense of Order - Positional Encoding

Transformers process all words in parallel (at the same time), which is efficient but creates a snag: Without order, "The cat chased the dog" looks the same as "The dog chased the cat." Order matters!

Let's build this understanding step by step:

First Principle: Sequences Need Structure: From Module 2, words are vectors. But vectors alone don't carry position info—like shuffled puzzle pieces.
The Fix: Positional Encoding: Add a special vector to each word's embedding that encodes its position (1st, 2nd, etc.). It's like stamping a "page number" on each piece.

Token and Position Embeddings

Analogy Escalation: Tear pages from a book and scatter them. Without numbers, no story flow. Positional encoding renumbers them invisibly, restoring sequence without changing content.

Gradual Intuition: Start small—number words in a sentence: "The (1) cat (2) sat (3)." Add that to embeddings, and the model "feels" the order in its math. Scale up, and it handles paragraphs or books.

Text Processing in an LLM