How do you distill a large LLM into a smaller one, and what are the approaches?
Distillation gives you most of a frontier model's quality for a fraction of the serving cost. The signal is naming the three LLM-specific variants and knowing which one holds up against a closed API. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Distillation gives you most of a frontier model's quality for a fraction of the serving cost. The signal is naming the three LLM-specific variants and knowing which one holds up against a closed API. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
