Rippling

Rippling runs an open-ended format where you build features incrementally against test cases the interviewer supplies along the way. It's a sequence of small, verifiable steps rather than one big build — and each step gives the interviewer a concrete artifact to grade.

The feedback that defines this loop

A Rippling candidate got explicit feedback that they "relied too heavily on AI even though their initial approach was correct." They knew what to build, but they let the AI make the implementation decisions — choosing data structures, structuring the code, picking the approach — and that distinction is what cost them.

This is the cleanest public signal of what these interviews penalize: the AI can do the typing as much as you want. The decisions — what to build, in what order, with which structures, and how to verify it — are yours.

How to prepare

Tactical checklist

1. Treat each interviewer-supplied test set as a mini contract: agree on the cases before implementing.
2. Decide the data structures and algorithm before prompting each step.
3. Have the AI write the implementation of your decision — not the decision.
4. Run the tests after every step; the interviewer's cases are your ground truth.
5. When the AI drifts toward its own design, redirect it to yours.