LunaPromptsA candidate finishes the feature quickly. The generated code looks tidy. There is even a test. The interviewer’s useful question is now very specific: what did the candidate check before deciding the work was done?
If coding assistants are part of the job, an assessment can make that responsibility visible. The tool helps produce the patch. The candidate still needs to understand its behavior.
LunaPrompts brings AI coding tools into the assessment workflow so hiring teams can evaluate how candidates plan, direct, review, and debug AI-assisted work. The focus is the candidate's ability to produce and verify a useful change in the configured development environment.
Review an AI-generated API change
Engineering interviewerIllustrative scenario01 / 03Constrain the change
Add a validation rule to an existing endpoint without changing its success response.
Give the candidate a small endpoint with a missing validation rule. Supply the expected error behavior and an existing success response. State which assistant is available and make sure access works before the timer begins.
The candidate can use the assistant to propose a patch. During review, look for evidence that they inspected the project context, constrained the change, and read the resulting diff. A generated patch that fixes validation but renames a response field introduces a useful discussion about interface ownership.
Ask, “Which behavior did you preserve, and how did you check it?” A strong explanation can point to a request, an assertion, or a focused diff. If the answer is simply that the assistant ran tests, ask which tests and what they establish. Existing tests may not cover the changed contract.
Keep the assessment comparable. Give candidates the same relevant repository state, instructions, time allowance, and tool access. Do not silently reward access to a better model or an already configured personal environment. When the role requires a specific tool, explain that requirement; when it does not, keep the rubric focused on engineering decisions.
What does an AI coding assessment reveal?
A practical task can reveal decisions that disappear in a final code submission. Does the candidate inspect the existing project before asking for changes? Do they describe the expected behavior? Do they read the generated diff? When a test fails, do they investigate the failure or request another broad rewrite?
These choices affect whether an AI-assisted developer can work responsibly in a real codebase. The assessment should make room to observe them.
Tools should serve the task
LunaPrompts supports assessment workflows involving Cursor, Claude Code, and Codex. Teams evaluating Antigravity skills can discuss the appropriate assessment setup. Establish the tools, versions, and access available in your environment before inviting candidates.
Tool names help candidates understand the environment, but they should not become the entire evaluation. A familiar interface is useful. A repeatable ability to understand a problem, constrain a change, and validate the result is more valuable to the hiring manager.
Give candidates a clear environment guide so they know which tools are available and how those tools fit the task.
Evaluate the full development process
Planning and context
Ask the candidate to inspect the relevant files, identify the expected behavior, and define a manageable change. Good context can include interfaces, constraints, existing tests, and the parts of the application that must continue to work.
Prompting and iteration
Look at whether instructions are specific enough to guide useful work. A candidate should be able to respond to an incomplete result with a targeted correction and explain why the change is needed.
Code review and verification
Review how the candidate checks the generated output. Working code should still be examined for edge cases, unintended behavior, and maintainability. The candidate should be able to explain what they tested and what remains uncertain.
Debugging and ownership
Introduce a relevant failure or ask a follow-up about one. The useful question is whether the candidate can take responsibility for the implementation, including parts an assistant helped create.
A review framework your team can use
The following is an editorial checklist for this workflow. Adapt it to the role, the candidate journey, and your configured environment.
| Review area | What to establish |
|---|---|
| Context | Inspects the relevant files and behavior |
| Direction | Gives the assistant a bounded task |
| Review | Finds unintended changes in the diff |
| Verification | Checks both the new requirement and preserved behavior |
A polished prompt is not evidence that the resulting code is correct. Review the work it produced and the candidate’s verification.
Put the guide to work
Choose a small bug in an example repository. Write one acceptance criterion and one behavior that must remain unchanged, then use both in the review.
Download the editable worksheet to record the decisions and open questions with your team. The examples in this guide are illustrative, not customer results or validated selection rules. Product availability and setup depend on the workflow agreed for your account.
A focused checklist, a worked scenario, and questions to resolve together.
Frequently asked questions
Should candidates be allowed to use AI in every coding assessment?
That depends on the skill you intend to measure. An AI-assisted task can reveal supervision and verification skills. A separate independent task can establish foundational coding ability. Explain the rules for each.
Is familiarity with Cursor or Claude Code enough to pass?
No. Evaluate the candidate's work against the role's criteria. Knowing a tool's interface is only one part of using it effectively.
How do we compare candidates using different tools?
Keep the task, expected outcome, time allowance, and scoring criteria consistent. Account for differences in tool access before treating the results as comparable.
Keep building your hiring workflow
- AI skills: The answer looks right. Can your candidate explain why?
- Coding assessments: The code passes the example. What happens on the second event?
- AI Viva: They have the answer. Ask what would change it.
Explore ai coding tools with LunaPrompts or browse all 19 hiring field guides.
