llm_interview_note

Why we included this project

For anyone prepping for LLM engineering interviews, this repo collects the scattered material you'd otherwise have to pull from a dozen papers and blog posts into one place. It moves through the stack an LLM engineer actually works with, from tokenization and Transformer internals to distributed training, LoRA-style fine-tuning, inference frameworks like vLLM, RLHF/PPO, and RAG. Each section pairs the concepts with realistic interview questions, so it works as a refresher before a technical screen and as a way to find the gaps in your own knowledge. The author also points to hands-on companion projects, including building a small Chinese LLM from scratch and a simple RAG system, which gives you a path from reading to actually doing. For self-taught learners and engineers moving into LLM work, it's a free, practical curriculum that saves hours of digging.