Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Supervised Fine-Tuning, 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
Explain what supervised fine-tuning accomplishes versus what pre-training establishes
Compare full fine-tuning, adapter tuning, and LoRA on the axes of cost, flexibility, and performance
Explain chat templates and why format consistency between fine-tuning and inference matters
Describe common SFT failure modes: catastrophic forgetting, format collapse, and reward hacking
Topics covered
SFT vs Pre-TrainingSFT DataChat TemplatesFull Fine-TuningSFT Failure ModesPEFT MotivationAdapter TuningLoRALoRA vs AdaptersQLoRAAdaLoRAMethod ComparisonSFT TrainingPEFT Variants
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 is supervised fine-tuning, and how does it differ from pre-training?
“How do adapter modules work — where are they inserted in a Transformer, and what makes them parameter-efficient?
“What is AdaLoRA, and what limitation of standard LoRA does it address?