Take-Home Assignment AI Helper — Unproctored Coding Tasks

Take-home assignments are unproctored, so the question isn't detection during the task — it's whether your submission looks like your own work. Evaluators increasingly check for AI-generated code, plagiarism and a believable commit history.

How take-homes are actually judged

• Code quality and correctness — the primary score.
• Plagiarism / similarity — against public repos and other candidates.
• AI-detection heuristics — generic structure, over-commented boilerplate, uniform style.
• Follow-up discussion — many teams ask you to walk through and extend your code live.

Making AI-assisted work your own

Use AI to draft, then refactor into your style: your naming, your structure, real commit history built as you go (not one giant commit), and comments that sound like you. Remove tell-tale generic boilerplate. Most importantly, understand every line — the follow-up call is where copied code falls apart.

The follow-up is the real test

Teams increasingly pair take-homes with a live walkthrough. If you can explain your design choices, handle “what if we change X,” and extend the code on the spot, the assignment passes regardless of how you drafted it. Use AI to understand, not just to produce.

FAQ