Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Transformer Architecture & Tokenization, pitched at the easy 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
Explain the encoder-decoder vs decoder-only architecture distinction and why modern LLMs are decoder-only
Describe how self-attention works at a conceptual level including query, key, and value matrices
Explain tokenization strategies (BPE, WordPiece, SentencePiece) and how they affect model input
Describe positional encoding and why it is necessary in Transformers
Topics covered
Transformer overviewTokenization basicsTokenization granularitySelf-attention intuitionArchitecture familiesPositional encodingBPE tokenizationWordPiece vs BPEQKV mechanicsScaling in attentionAttention softmaxSinusoidal positional encodingSentencePieceCausal masking
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“At a high level, what problem does the Transformer architecture solve that earlier sequence models like RNNs struggled with?
“The original Transformer paper introduced sinusoidal positional encodings. What makes this approach appealing compared to simply learning position embeddings from scratch?
“Suppose you are tokenizing code and the model frequently splits common keywords like 'return' or 'def' into multiple tokens. What causes this, and how would you address it?