fine tuning

A strategic decision with a clear default and specific reasons to deviate. The interviewer wants a framework grounded in evals, volume, constraints and team capacity.

Recommend an approach to a CEO who needs a product decision. Explain what retrieval and fine-tuning each change, then connect that difference to freshness, reliability and cost.

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.

Choose a training objective from observed failures. Separate preference labels, demonstrated answers and adapter methods from factual grounding.

Hundreds of examples in a cached prompt can match fine-tuning and let you change behaviour in minutes. Price both, then decide by volume, latency and how often the examples change.

A platform question at principal level. Every team running its own training and serving stack wastes GPUs and creates risk; a central platform that is hard to use gets bypassed. The design has to make the shared path the fastest one.

A principal-level platform design. Customers want better results on their tasks; most of them would get there faster without fine-tuning. The product has to guide them, protect their data, preserve safety, and survive base model upgrades.

Customers running fine-tuned models in production depend on the base model staying alive. Stage the wind-down, keep inference running until the base retires and give each segment a path forward.

Custom models are sometimes right and usually premature. Show a ladder of cheaper options, the evidence that would justify climbing it, and how you kept the customer while saying no.