How do you turn token embeddings into a single sentence/document embedding (pooling)?
A transformer emits one vector per token, but retrieval needs one vector per text. The signal is knowing the pooling options and the catch nearly everyone overlooks: the model has to be trained for whichever one you pick.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A transformer emits one vector per token, but retrieval needs one vector per text. The signal is knowing the pooling options and the catch nearly everyone overlooks: the model has to be trained for whichever one you pick.
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.
