How do you evaluate a postfix expression?
PICTURE THIS: A SENTENCE BECOMES TOKENS
The model does not read letters like humans. It reads these pieces, then predicts the next one.
Simple meaning
Scan tokens, push numbers, pop operands for operators.
WHY — Stack instead of guessing?
Why interviewers care about Stack:
question about Stack.
trade-offs, and what you would actually do on a DSA project - not buzzwords.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens with tokens?
Before the model can read a sentence, it goes through these steps:
- 1Scan tokens, push numbers,
pop operands for operators.
- 2Stack depth errors mean
invalid input.
- 3Embeddings
O(n) time.
- 4Context mix
Attention looks at nearby tokens together.
- 5Next token
The model scores what should come next.
- 6Decode
IDs turn back into readable text.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Scan tokens, push numbers, pop operands for operators. Stack depth errors mean invalid input.