Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Pre-Training, 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
Describe the data curation pipeline for pre-training: deduplication, quality filtering, and tokenization
Explain scaling laws and what they predict about compute-optimal training (Chinchilla)
Describe the pretraining objective: causal language modeling and next-token prediction
Explain mixed-precision training (BF16/FP16), gradient clipping, and AdamW optimizer choices
Topics covered
Pre-training objectiveData curationTokenizationScaling lawsTraining optimizationDistributed trainingEnd-to-end design
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 does pre-training actually do for a large language model, and how is it different from fine-tuning?
“What are neural scaling laws, and what does it mean to say that model performance scales predictably with compute, data, and parameters?
“What is the difference between FP32, FP16, and BF16 in terms of their numerical range and precision, and why does that matter for training stability?