# Assess coding with the tools people actually use

https://lunaprompts.com/solutions/ai-assisted-coding-assessment

Illustrative assessment template. Adapt and calibrate for the role before use.

Allow the tools the role needs. Evaluate the candidate’s review, debugging, and judgment alongside the code they deliver.

## Task

Give the candidate a small repository with a pagination bug and an incomplete test suite. Ask them to fix the bug, add regression tests, and explain an AI-generated change they accepted or rejected. Agree the available editor and assistant before the session.

## Deliverables

- A focused patch with a reproducible regression test
- Notes explaining a suggestion that required correction
- A short walkthrough of edge cases and remaining limitations

## Suggested rubric

### Working change — 30%
Stronger evidence: Fixes the bug without breaking the existing behavior.
Investigate: Handles the example but fails boundary cases.

### Tests and verification — 30%
Stronger evidence: Demonstrates a failing test before the fix and meaningful regression coverage.
Investigate: Accepts generated tests without checking what they assert.

### Review of AI output — 25%
Stronger evidence: Spots an incorrect assumption and corrects it deliberately.
Investigate: Cannot explain why the generated change is safe.

### Communication — 15%
Stronger evidence: Explains the patch and unresolved tradeoffs in their own words.
Investigate: Repeats the assistant’s explanation without connecting it to the code.

## Illustrative report (not a real candidate result)

The sample patch passes its tests, but a generated test mocks away the boundary condition that caused the bug.

Ask the candidate to rewrite that test and explain the missing signal. Completion speed alone is insufficient evidence.

## Follow-up questions

- Which assistant suggestion did you reject, and why?
- Show the test that would fail if this bug returned.
- What happens when the input is empty or the last page is full?

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