You're training embeddings with contrastive/triplet loss. How do you choose pairs, the margin, and negatives?
Metric learning succeeds or fails on the pairs you feed it. Random negatives teach almost nothing, and the margin plus the mining strategy determine whether the embeddings are any good. Here is how the choices interact.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Metric learning succeeds or fails on the pairs you feed it. Random negatives teach almost nothing, and the margin plus the mining strategy determine whether the embeddings are any good. Here is how the choices interact.
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.
