AI / LLM Basics Interview Questions
What is Positional Encoding?
Positional Encoding is information added to each token's embedding to tell the model where that token sits in the sequence, since the attention mechanism on its own has no built-in sense of word order.
- Without it, a Transformer would treat "dog bites man" and "man bites dog" identically, since attention alone just relates tokens to each other regardless of position
- Typically implemented as a fixed or learned pattern of values added directly to each token's embedding
- Lets the model reconstruct sequence order without needing to process tokens strictly one at a time
This small addition is what allows the Transformer to keep its fast, parallel processing while still understanding that order matters in language.
Why is Positional Encoding needed?
What would happen without Positional Encoding?
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