The 60-second answer
Explain token embeddings plus positional information feeding repeated attention and feed-forward blocks. Self-attention forms Q, K, and V, computes scaled similarities, then mixes value vectors.
Build the answer in this order
1
Give the core idea
Explain token embeddings plus positional information feeding repeated attention and feed-forward blocks.
2
Explain how it works
Self-attention forms Q, K, and V, computes scaled similarities, then mixes value vectors.
3
Compare alternatives
Multi-head attention learns multiple interaction subspaces while residuals and normalization stabilize training.
4
State failure modes + validation
Call out quadratic attention cost with sequence length and masking requirements.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
01Definition
02Mechanism
03Trade-offs
04Failure modes
05When to use
Senior-level signal
- Discuss KV caching and efficient attention kernels for serving.
- Compare encoder-only, decoder-only, and encoder-decoder objectives based on the task.
What the interviewer is really testing
Mechanistic understanding, assumptions, trade-offs, and whether you can turn a definition into a model decision.
Likely follow-up questions
What assumption makes this approach work?
When would you choose the strongest alternative instead?
What production or data failure mode changes your answer?
Common weak-answer patterns
- Reciting a definition without mechanism or assumptions.
- Claiming one technique is always better without a data regime.
- Stopping before failure modes, validation, or deployment implications.