How do efficient attention variants (sparse, sliding-window, linear) make long context feasible?
Full attention is O(n^2), so long context demands cheaper attention. What shows depth is naming the families (sparse, sliding-window, linear), what each surrenders, and why FlashAttention is not one of them.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Full attention is O(n^2), so long context demands cheaper attention. What shows depth is naming the families (sparse, sliding-window, linear), what each surrenders, and why FlashAttention is not one of them.
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.
