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How do efficient attention variants (sparse, sliding-window, linear) make long context feasible?

Full attention is O(n^2), so long context needs cheaper attention. The signal is naming the families (sparse, sliding-window, linear), what each gives up, and why FlashAttention is not one of them.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Full attention is O(n^2), so long context needs cheaper attention. The signal is naming the families (sparse, sliding-window, linear), what each gives up, and why FlashAttention is not one of them.

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These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·How language models work, enough to build onFree14mAttention: how a model decides which words matter to whichYou do not need the maths of attention to build with models, but you do need what it buys and what it costs. This lesson gives you the intuition and the one consequence that shapes every design decision later: cost grows with the square of the input.

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