The quality of your assessment determines the quality of your signal. A well-designed question tells you exactly what you need to know about a candidate’s ability to do the job, but a poorly designed one just produces noise.
These guidelines apply whether you’re writing questions from scratch or editing a generated assessment.
Guiding Principles
The core of what makes a good interview question is that it creates nearly immediate behavioral divergence between poor, good, and great candidates.
Aptora relies on behavioral telemetry to grade, which means the first few minutes of a candidate’s assessment can be the most important. How did they approach breaking down the problem? Did they start coding immediately or do some research first? Did they do any necessary exploration and investigation?
In order to build questions that create this divergence, we recommend a few formats:
- Easy Baseline, Infinite Ceiling - Create a question that is easy to do poorly, but very difficult to do correctly. Our tutorial question is a great example of this. It’s easy to create an in memory database, but impossible to build PostgreSQL in an hour.
- Require Deep Investigation - Create hidden services that are black boxes to candidates. Force investigation where great candidates will show their curiosity.
- Too Much to Do - Create a question that forces prioritization. Give candidates a business case and see what they focus on first. In the AI era, programming skills are a commodity but design taste is more important than ever.
What to Avoid
The common thread: anything that flattens the first few minutes so that weak, average, and strong candidates all look identical to the telemetry.
- Single-answer puzzles - if there’s exactly one correct solution and getting there is the whole game, you’ve capped the ceiling. Strong candidates have nowhere to differentiate themselves once they’ve solved it.
- Fully specified prompts - when every input, output, and edge case is laid out upfront, there’s no surface for investigation or curiosity. The candidates who would otherwise probe and explore look the same as the ones who just start typing.
- Narrow-scope tasks - if there’s only one thing to do, prioritization never enters the picture. You lose the signal on design taste and judgment, which is exactly what matters most in the AI era.
- Trick questions - if a hidden gotcha gates the entire problem, you’re measuring whether candidates have seen the trick before, not how they think. This is the opposite of behavioral divergence — it’s a binary pass/fail on trivia.
- Pure algorithmic puzzles - even when they’re hard, they reward memorized patterns rather than investigation, prioritization, or taste. Strong candidates increasingly read them as a negative signal about your engineering culture.
- Problems with implicit requirements - if you expect a specific language feature, library, or approach, say so. Otherwise you’re punishing candidates for diverging in ways you didn’t intend, which is noise, not signal.
Support
Questions about assessment design? Reach out at johnny@tryaptora.com.