Interview prep
AI/ML interview blind spots: the questions you don’t know to ask yourself
Most prep only tests what you already thought to review. This is why that leaves gaps, and how to find them before an interviewer does.
The prep you do is the prep you already thought of
If you sit down to prepare for an AI/ML interview, you almost always study the topics you already suspect are weak, or the ones a study guide told you to cover. That is useful, but it has a structural blind spot: it can only ever test what you already thought to ask about.
The gaps that actually hurt in an interview are rarely the ones you were nervous about going in. They are the adjacent concept an interviewer’s follow-up question exposes — the one you did not rehearse because you did not know it was connected.
Why free LLM chat does not fix this
Asking an LLM to quiz you has the same shape of problem. It answers the prompt you give it. If you ask about attention mechanisms, it will explain attention mechanisms — it will not necessarily probe whether you understand the memory tradeoffs of KV caching that a real interviewer would jump to next.
The result is prep that feels thorough because you covered a lot of ground, without any signal on whether the ground you covered was the ground that mattered.
What actually finds a blind spot
Finding a blind spot requires adaptive probing: starting from what you say, then following shallow or uncertain answers into the prerequisites and adjacent concepts an interviewer would naturally ask about next. That is a different mode than static Q&A — it requires the follow-up to be shaped by your answer, not a fixed script.
Assessmentr runs this as a voice-first diagnostic: you explain a topic out loud, the probing follows your answer into what it actually implies, and the output is a ranked gap map — not a list of questions, but the one concept most likely to hurt you if it came up tomorrow.
Find your own gap map.
Free during the beta — one voice diagnostic, one ranked gap, one next action.