Practiced one out loud? Paste your answer into the free self-check and get a score in 30 seconds
What the interviewer is testing
Whether you can explain the building blocks clearly in under two minutes, connect self-attention to why transformers replaced recurrent models, and handle the standard follow-ups (decoder-only, the sqrt(d_k) scaling, quadratic cost).
Answer framework
- Start with the shape: a stack of identical blocks.
- Inside each block: multi-head self-attention, then a position-wise feed-forward network (MLP), each wrapped in residual connections and layer normalization so deep stacks train stably.
- Explain self-attention: each token is projected into a query, key and value; weights are softmax(QK^T / sqrt(d_k)); the output is the weighted sum of values.
- Say why it matters: any token can pull information from any other token in one step, and training runs over the whole sequence in parallel.
- Describe modern LLMs: decoder-only with causal masking, trained to predict the next token, with positions from rotary embeddings (RoPE).
- Name the trade-off: attention cost is O(n^2) in sequence length.
Sample answer
A transformer is a stack of identical blocks. Each block has multi-head self-attention, which lets every token mix information from other tokens, and a position-wise feed-forward network. Residual connections and layer normalization wrap both sub-layers so deep stacks train stably.
In self-attention each token is projected into a query, key and value. The attention weights are softmax(QK^T / sqrt(d_k)), and the output is the weighted sum of the values. Long-range dependencies do not have to pass through a recurrent bottleneck, and the whole sequence is processed in parallel during training. The cost is O(n^2) in sequence length for compute and for the attention matrix.
Most modern LLMs are decoder-only: causal masking means each token attends only to earlier tokens, and the model is trained to predict the next token. Position information comes from positional encodings, today usually rotary embeddings (RoPE).
Likely follow-ups
Common mistakes
- Describing the original encoder-decoder design as if it were how most current LLMs are built.
- Leaving out residual connections and layer normalization, which are why deep stacks train.
- Writing the attention formula without being able to explain the sqrt(d_k) scaling.
- Not mentioning the O(n^2) cost in sequence length, which drives many long-context design choices.
- Forgetting where position information comes from.
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