What is a transformer model?
PICTURE THIS: TINY NEURAL NET
Simple meaning
A transformer is a neural architecture that uses attention to relate all positions in a sequence in parallel.
WHY — Tokens instead of words?
LLMs use tokens (not full words) because it helps them:
who only read docs from people who shipped.
and tied to GenAI / LLM work.
Each piece maps to a number the network can learn.
Fixed pieces are what transformers expect as input.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1A transformer is a
neural architecture that uses attention to relate all positions in a sequence in parallel.
- 2It replaced recurrence as
the default for large language models.
- 3Stacks of attention and
feed-forward layers learn contextual representations.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
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
A transformer is a neural architecture that uses attention to relate all positions in a sequence in parallel. It replaced recurrence as the default for large language models.