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Core
Knowledge Distillation
Knowledge distillation trains a small student model to copy a larger teacher, treating the teacher's soft probability distribution (or internal features) as a richer training signal than hard labels. A student trained this way usually outperforms an identical model trained from scratch on the same data, because the soft targets carry the teacher's learned similarity structure. AI, ML, and GenAI engineer interviews probe it because it is the main lever for compressing a capable model into something cheap to serve, and because reasoning distillation and the legal terms around teacher outputs are live issues in 2026.
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ML Infrastructure & GPUsExplain knowledge distillation: how it works, when to use it, and how it compares to quantization and pruning.→LLM & GenAI FundamentalsHow do you distill a large LLM into a smaller one, and what are the approaches?→ML Infrastructure & GPUsYour model training is burning a lot of energy. How do you measure and cut the carbon footprint?→LLM & GenAI FundamentalsCompare distillation recipes for LLMs: hard-label SFT, on-policy logit matching, and rejection sampling.→
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