A coding take-home is easier to judge consistently when candidates and reviewers share the same explicit standard. Morgan Zhou’s proposed packet pairs the prompt with an executable rubric, a deliberately flawed sample solution, and a short explanation of the sample’s failures. That makes the expected contract visible instead of leaving everyone to guess at an unstated ideal.
What the “wrong answer” is for
The idea is not to trick candidates with a broken reference or to treat the sample as the only acceptable design. It is to make the assessment’s boundaries concrete: candidates can see what a known-bad implementation gets wrong, while reviewers can check submissions against published requirements. Zhou describes four files that travel together: the candidate prompt, a machine-checkable rubric, a sample solution that is wrong on purpose, and a catalog of its failures. Morgan Zhou’s article presents this as a proposed hiring practice, not as a method proven by a controlled study.
The exercise should test a small, job-relevant contract—not stand in for every engineering skill or become a miniature system-design project. The U.S. Office of Personnel Management defines work-sample tests as tasks that mirror work activities employees perform. Its guidance says they are most appropriate when the assessed competencies are critical and expected at entry; a skill that will be taught after hiring may not belong in a pre-hire work sample. OPM’s work-sample guidance supports that qualification, but does not evaluate Zhou’s particular packet.
What the example asks candidates to build
The example prompt asks for a local HTTP service listening on port 8080. A POST /review request supplies JSON fields diff, tests_passed, tests_failed, and secrets_hit. The response contains a score, a verdict (reject, revise, or pass), reasons, and beats_sample.
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The point is not to infer what “good code” means from a reviewer’s preferences. The prompt states behavioral invariants, and the grader checks them:
- If tests failed, the service must not return a passing verdict.
- If
secrets_hitis true, the score is capped at 20 and the verdict must be rejection. - Reasons must cite a concrete signal in the submitted payload rather than offer a vague explanation.
- The response must report whether the implementation beats the known-bad sample, as part of the scoring contract.
The packet also asks for grade_receipt.json containing one request and its response actually run. That gives reviewers a small, inspectable example of the implementation’s behavior; it is not a substitute for running the grader.
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How the known-bad sample makes the rubric legible
The deliberately poor sample always returns score 100, verdict pass, and a vague reason. That exposes failures a candidate can reason about: it ignores failed tests, fails to apply the secret-related cap and rejection rule, and does not give a useful payload-specific reason. The proposed direction sample instead applies the relevant caps and gives concrete reasons for failed tests or a secret flag.
The article’s examples illustrate the intended checks; they are not independently run code results. The useful design principle is that the sample should fail in documented, observable ways. Reviewers should run it against the same grader candidates face, so the reference point is not merely decorative and the rubric is not silently interpreted differently in practice.
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Build a take-home packet that candidates can actually complete
- Choose a narrow task tied to entry-level work. Identify the competency the role requires on day one, then design a small task that exercises it. Avoid using a puzzle simply because it is easy to grade.
- Write the prompt as an observable contract. Specify the interface, required inputs and outputs, and rules that can be checked. In Zhou’s example, port 8080,
POST /review, the JSON fields, score cap, verdict rules, and concrete-reason requirement make the expectations inspectable. - Turn the contract into an executable rubric. Include cases that distinguish a compliant result from a plausible-looking failure. The example checks that failed tests cannot yield a pass and that a secret-bearing payload is rejected under the score cap.
- Ship a known-bad sample and explain each failure. Make its defects specific enough that candidates can understand what the rubric detects, without implying there is only one valid implementation.
- Run the grader against a live local process. Use the same host, timeout, and payload bytes for the sample and candidate implementation. Include an actual request-response receipt rather than a hand-written claim that the service works.
- Keep the environment accessible. The example is meant to be feasible with a free model and a free local machine. Do not require Kubernetes, dashboards, paid vendor logins, paid API calls, a GPU, private datasets, or production credentials.
- Bound the candidate’s time and data exposure. Do not turn the assignment into unpaid weekend work. If the organization cannot accept or appropriately handle candidate code, do not ask candidates to submit it.
Where this approach fits—and where it does not
A published grader can reduce ambiguity about whether specific behaviors meet stated requirements. It does not, by itself, establish that the task predicts job performance, is fair across candidates, or measures the right competency. OPM’s general assessment guidance describes validity estimates of 0.54 for work-sample tests and 0.51 for structured interviews; the page does not state a year for those figures. They describe general assessment methods, not this four-file packet, and should not be read as an outcome claim for Zhou’s proposal. OPM’s assessment-strategy guidance explains validity as the relationship between assessment performance and job performance.
For a consistent process, the rubric is only one part. OPM describes structured interviews as using standardized questions and common rating standards, which can give candidates equal opportunities to provide information and support consistent assessment. A coding work sample is a different instrument, but the same discipline is useful: apply the same prompt, checks, and rating standards to every candidate, and avoid hidden criteria that override the published ones.
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Most importantly, public checks must not be a decoy for secret rescoring. If reviewers add undisclosed preferences after seeing submissions, the known-bad sample has not made evaluation transparent. The packet’s value depends on reviewers treating its stated contract as the standard and being able to explain any additional assessment separately.
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