You need a model but only have a few hundred labeled examples. How do you build one anyway?
A small labeled set is the normal place to start, not an excuse. The strong answer is a ladder of techniques that pull signal from unlabeled data, pretrained models, and the labeling budget. Here it is.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A small labeled set is the normal place to start, not an excuse. The strong answer is a ladder of techniques that pull signal from unlabeled data, pretrained models, and the labeling budget. Here it is.
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.
