model selection

Fine-tuning is sometimes the best decision a team makes and often a costly detour. The interviewer wants the test that tells them apart, with numbers.

A vocabulary check on one of the most important model developments of recent years. The good answer explains the trade (accuracy for time and money) and gives a rule for when it pays.

A trade-off question where both sides have serious arguments. The interviewer wants the dimensions that decide it, and a recommendation that depends on facts you would go and get.

There is no single best model, and the choice will change within a year. Principal answers design a selection process and an architecture that make the next choice cheap.

Saturated and contaminated benchmarks separate models less than the noise in your own traffic. Use public scores to shortlist and a private, refreshed eval to decide.

Leaderboards move every few weeks and every team feels the pull. The interviewer is checking whether you have a repeatable switching process or just enthusiasm.

Opinions about prompts are cheap. A small grid of prompts by models on fifty real examples turns a debate into a table, and the interviewer wants to see you build it.