Easy Attention Question 18 of 223

What is multi-head attention?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: AN LLM TURN

Text inTokens

TransformerAttention

Text outNext token

Simple meaning

Multi-head attention runs several attention operations in parallel with different projections.

WHY — Tokens instead of words?

LLMs use tokens (not full words) because it helps them:

Attention questions separate people

who only read docs from people who shipped.

Keep it short, concrete,

and tied to GenAI / LLM work.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

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:

1
Multi-head attention runs several
attention operations in parallel with different projections.
2
Each head can specialize,
for example on syntax versus long-range reference.
3
The heads are concatenated
and linearly combined.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“Each head can specialize, for example on syntax versus long-range reference.”

Break into beats

Eachheadcanspecializeforexample

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

Multi-head attention runs several attention operations in parallel with different projections. Each head can specialize, for example on syntax versus long-range reference.