Interview questions · Fine-Tuning Interview Questions (LoRA, RLHF, DPO)
Fine-Tuning Interview Questions (LoRA, RLHF, DPO)
Fine-tuning questions test judgment: when to fine-tune vs prompt vs RAG, and the mechanics — SFT, LoRA/QLoRA, DPO, distillation.
Practice these free — flashcards, retrieval checks & a timed mock interview.▸ Open the Applied AI / Forward Deployed Engineer trainer
Key concepts to know
- Climb the ladder in order: prompt → few-shot → RAG/tools → fine-tune.
- Fine-tuning changes behavior/format/skill, not knowledge — use RAG for facts.
- Fine-tune only after prompting/RAG plateau, when you have enough quality examples.
- SFT = train on ideal completions; LoRA/PEFT = cheap adapters (modern default); DPO/RLHF = preference/behavior tuning.
- Distillation = teacher model trains a cheaper student for prod cost/latency.
- Success = data quality; beware hosting costs, re-tuning, and catastrophic forgetting.
Interview questions & answers
Which is the BEST reason to fine-tune (rather than prompt/RAG)?
To reliably enforce a specific output style/format or a narrow skill that prompting can't hold consistently.
Why: Fine-tuning shines for consistent behavior/format/narrow skills. Volatile facts (inventory, prices, daily docs) belong in RAG, not the weights.
You want cheap, fast task-specific fine-tuning without paying to update all weights. Use:
LoRA / PEFT — train small adapter matrices instead of all weights.
Why: LoRA/PEFT trains small adapters rather than the full model — cheaper, faster, swappable per task. That's the standard efficient fine-tuning route.
Go deeperPractice the full track in the free interactive trainer — spaced-repetition flashcards, retrieval checks and timed mock interviews.