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To adapt an LLM evaluation exam to a different pipeline, keep the testing method but replace its reference data and failure scenarios with those from your own workflow. Start by naming the most serious consequence of an AI error that cannot readily be undone; use that risk to determine the questions, expected answers, and human handoffs in the exam.
What changes when you port an exam to another pipeline?
An exam designed for reading orders does not automatically test email triage, meeting-minutes summarization, research, or data cleanup. The portable part is the discipline of building questions around consequential errors. The domain-specific parts are the reference data and the worst outcome to prevent.
John Green’s August 25, 2026 DEV Community article uses wrong goods loaded onto a truck as its order-reading example. For another workflow, substitute the consequence specific to that workflow: an incorrect customer reply being sent, an undecided matter being recorded as a decision, an invented number entering a report, or original data being overwritten without recovery. These are illustrative risks, not claims that every deployment will experience them.
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How to design the exam around real failure modes
1. Name the accident that cannot readily be undone
Write down the action or outcome that would be hardest to reverse if the model were wrong. Green frames the starting question as: “When this AI is wrong, which of the consequences cannot be undone?” This gives the exam a risk-based focus instead of an arbitrary goal such as collecting a fixed number of test cases.
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2. Grade errors by whether a person can still recover
Use severity categories that distinguish irreversible errors from mistakes a human can still catch. Green’s categories are FATAL (cannot be undone), RISKY (an ambiguous case was confirmed), MISSED (a human can still catch the dropped item), and HARMLESS (an unnecessary confirmation adds delay). The important distinction is that a confident but wrong confirmation can be worse than withholding confirmation for review.
3. Write trap questions as well as routine examples
Ordinary examples show whether the system can handle expected inputs; traps reveal whether it fails in ways that matter. Include realistic cases such as:
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- Confusable pairs that differ in a detail the model must notice.
- Plausible non-targets that resemble valid items but should not be accepted.
- A message that changes direction partway through, so the latest relevant information must be interpreted correctly.
- For systems with memory, cases where fresh information must override remembered information, plus cases where memory should not change the verdict.
Build at least one question for every identified accident type, adding variants where they help test the same risk. Then include a smaller set of ordinary examples. Let the accident list determine coverage rather than aiming for a test count with no connection to risk.
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4. Write the answer key from reference data, not intuition
For every test input, write the expected answer and check whether the reference data supports exactly one answer. If the input is genuinely ambiguous, the key should expect confirmation or handoff rather than reward a confident guess. When the model and key disagree, inspect the reference data before deciding that the model is at fault; the key may be ambiguous or incorrect.
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Green’s formulation captures the division of work: “The exam you may steal. The answer key you have to write yourself.” Your pipeline’s own data and uncertainty rules determine what counts as correct.
5. Grade outcomes, retain outputs, and record limits
Count results by severity, not only as a single pass rate. Preserve raw model answer sheets so the team can apply revised grading criteria later without rerunning the model. Document what the exam verified, what production data it did not test, and which uncertain cases must go to a person.
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What a passing score does—and does not—show
A passing exam shows performance on the cases the team anticipated and wrote down. It does not establish safety on unimagined inputs or prove that incidents will fall in production. Green notes that the production data discussed in the article had not been tested in the described exam, and that its example sentences were invented. The method is practical guidance from one author’s account, not an independently validated cross-domain study or a quantified claim of incident reduction.
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That limitation makes the coverage record part of the result, not administrative overhead: it tells reviewers which risks were exercised and where a human guardrail remains necessary. As Green puts it, “Without line three, an exam score is just a number — the first unimagined sentence causes the accident.”
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Source and optional test runner
The method and examples are from John Green’s August 25, 2026 DEV Community article. The article also links a public software test runner and grader; it does not establish that the repository is a paid service.
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