How do you curate and filter a supervised fine-tuning (SFT) dataset, and why does a smaller clean set often win?
A few thousand carefully chosen examples can beat a million scraped ones. The signal is knowing which filters matter, how you gauge example quality, and why diversity beats raw volume.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A few thousand carefully chosen examples can beat a million scraped ones. The signal is knowing which filters matter, how you gauge example quality, and why diversity beats raw volume.
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.
