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NLP interview questions cover tokenization, text preprocessing, chunking for retrieval, and the sequence handling that language model applications depend on.
Natural language processing questions in engineering interviews centre on the unglamorous layer beneath a model: how text becomes tokens, how documents are split for retrieval, how sequences are batched and padded. These are the parts an engineer actually owns in a production language-model application, which is why they are asked.
Tokenization is the highest-yield area because everything downstream is denominated in tokens — cost, context limits, throughput — and because implementing byte pair encoding is a clean, self-contained exercise. For retrieval applications, interviewers probe chunking strategy specifically, since chunk size and overlap usually explain more about a system's answer quality than the choice of embedding model does.
2 system design · 13 coding · 3/7/5 easy/medium/hard
Because it determines cost, context limits and throughput in any language model application, and because implementing byte pair encoding is a well-bounded coding question with a real data-structure core.
Chunking at a fixed character count instead of along the document's own structure. Coherent chunks with a little overlap, carrying their section context, usually improve answer quality more than changing the embedding model.