AI Glossary
Positional Encoding
Information added to tokens that helps a model understand their order within a sequence.
Overview
Words rarely mean the same thing when their order changes.
Consider the difference between:
“The dog chased the cat.”
and
“The cat chased the dog.”
The same words appear in both sentences, but the meaning changes because of their position.
This creates an important challenge for AI models.
Many modern language models process tokens simultaneously rather than strictly one at a time. As a result, they need a way to understand where each token appears within a sequence.
Positional encoding solves this problem.
Positional encoding adds information about position to each token so the model can understand order and context. This allows the model to distinguish between words that appear at different locations within a sentence.
Without positional encoding, a model would struggle to understand many of the relationships that give language its meaning.
Together with tokenization, embeddings, and attention mechanisms, positional encoding helps modern language models interpret text more effectively.
Why It Matters
Positional encoding helps AI models understand word order and sequence relationships.
Real-World Example
When a chatbot interprets a sentence, positional encoding helps it understand how the order of words influences meaning.