Design a two-tower retrieval system for recommendation/candidate generation.
How large recommenders and search draw candidates from millions of items in milliseconds. The signal is why the user and item towers stay separate, how that enables precomputed embeddings plus an ANN index, and where ranking takes over.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
How large recommenders and search draw candidates from millions of items in milliseconds. The signal is why the user and item towers stay separate, how that enables precomputed embeddings plus an ANN index, and where ranking takes over.
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.
