How do RNNs, LSTMs, and GRUs work, and why did transformers largely replace them?
Sequence models remain interview staples, particularly the gating that repaired RNNs and the reason attention took over. What matters is linking the vanishing-gradient narrative to the parallelism case that let transformers ride the scaling wave.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Sequence models remain interview staples, particularly the gating that repaired RNNs and the reason attention took over. What matters is linking the vanishing-gradient narrative to the parallelism case that let transformers ride the scaling wave.
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.
