Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Transformer Architecture & Tokenization, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Distinguish Multi-Head Attention, Multi-Query Attention, and Grouped-Query Attention and explain the memory trade-offs
Explain the KV cache, what it stores, and why it grows with context length
Compare greedy search, beam search, temperature sampling, top-k, and nucleus (top-p) sampling
Explain Flash Attention and why it reduces memory complexity
Topics covered
KV CacheDecoding StrategiesMulti-Head AttentionAttention VariantsFlash Attention
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In plain terms, what is a KV cache and why does a decoder-only language model need it during generation?
“At a high level, what is FlashAttention trying to solve, and what is its core insight about how modern GPUs handle memory?
“You are designing a serving system for a large LLM and need to support batch sizes of 64 with 32K context. Walk me through the KV cache memory calculation and the tradeoff between batch size, context length, and number of KV heads.