7 min read How mixture-of-experts models split parameter count from compute per token, and the routing, memory, and serving tradeoffs interviewers test. Read article
6 min read A concrete look at what fine-tuning interviews probe about LoRA, QLoRA, and the cases where fine-tuning is the wrong tool. Read article
7 min read The agent design prompt is now standard in AI and infra loops, and it rewards candidates who reason about tool loops, memory, and failure instead of naming frameworks. Read article
7 min read A quick reference for the attention, tokenization, KV-cache, RAG, and evaluation questions that come up in LLM and machine learning interviews. Read article
6 min read The GenAI engineer loop looks like a machine-learning loop until you hit the retrieval design and evaluation rounds, where most candidates quietly fall apart. Read article
6 min read What separates a passing RAG design answer from a hand-wavy one, stage by stage. Read article
5 min read Retrieval-Augmented Generation (RAG) is one of the most widely deployed LLM patterns in production. Understanding when to use RAG versus Read article
6 min read One of the most common LLM interview questions in 2026: “Would you fine-tune a model or train from scratch?” Almost Read article
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