In an AI-assisted coding round, you're given an existing codebase of several files with tests, plus an AI assistant, and you have about an hour to work through one extended problem. It can include tasks like these: fix a bug, build the core feature, and optimize for large inputs.
It's one problem, so the tasks stack#
In a regular DSA round with two problems, a bad first problem doesn't affect the second; each one is a fresh start. Here, when you get more than one task, they build on each other. If you misunderstand the codebase while fixing the bug, that mistake carries into the feature, and a messy feature makes the optimization harder. That's why getting oriented at the start matters more in this round than in a regular DSA round.
The code runs#
In a regular algorithm round, execution is often disabled, so you prove correctness by reasoning through it. In this round you can run the tests, which means running them often becomes part of what you're scored on. Pasting AI-generated code and moving on without running it is a visible miss, in a way an untested hand-written solution in a no-execution round never was.
You're talking the whole hour#
You narrate your thinking for sixty minutes while also managing the AI. That's a stamina test as much as a skill test, and full-length mocks are the only way to train for it.
It sits next to a regular no-AI round#
The interview loop usually still includes a classic algorithm round where AI isn't allowed. This round doesn't replace DSA practice. The optimization task also depends on recognizing the right algorithm, so DSA practice stays in your routine.
The core idea: the round checks whether you can do real work with AI. That means understanding unfamiliar code, directing the tool, and catching its mistakes. It does not check whether you know prompting tricks.