Transformer Guide Sharpens AI Interviews

This is useful interview preparation because it tests reasoning about systems behavior, not memorized architecture diagrams.

• –Explains why dividing attention logits by √dₖ prevents softmax saturation and preserves gradients, as established in the original Transformer design ([Attention Is All You Need](https://arxiv.org/abs/1706.03762)).
• –Connects parallel training with the sequential dependency that makes autoregressive decoding slower.
• –Covers GQA as an inference optimization that reduces KV-cache memory and bandwidth while retaining multiple query heads ([GQA paper](https://arxiv.org/abs/2305.13245)).
• –Best suited to candidates preparing for model-training, inference, or ML systems interviews rather than general software-engineering screens.