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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To adapt an LLM evaluation exam to a different workflow, replace its reference data and redesign its questions around the worst mistake your system could make. Start by identifying the error that would be hardest to undo; then test the realistic situations that could trigger it, grade outcomes by severity, and route uncertain cases to a person instead of treating a guess as correct.
Start with the harm, not the test questions
John Green’s August 25, 2026 DEV Community article describes adapting an exam originally designed for order reading to other pipeline tasks. Its portable idea is to change two domain-specific elements: the reference data and the worst consequence of an error. The central question is: “When this AI is wrong, which of the consequences cannot be undone?”
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For an email auto-reply system, that might be sending an incorrect answer to a customer. For meeting-minutes summarization, it could mean recording a proposal as a decision. In research, it might be inserting an invented number into a report; in data cleanup, overwriting the original without a recovery path. These are examples, not a claim that every system will face those exact failures.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesDescribe the consequence as an observable action and result, not a vague category such as “bad output.” That gives the exam a target: the cases to include are the situations that might cause that action or make the result hard to reverse.
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Set severity grades around whether a person can undo the error
Use grades that distinguish the consequences of being wrong from the inconvenience of being cautious. Green’s categories are based on human undoability:
- FATAL: the error cannot be undone.
- RISKY: an ambiguous case was confirmed instead of being escalated.
- MISSED: the system dropped something, but a person can still catch it.
- HARMLESS: an unnecessary confirmation adds delay.
This framing makes a wrong confirmation more costly than withholding a confirmation when the input is unclear. As Green puts it, “a wrong confirmation is worse than no confirmation.” Define what confirmation, escalation, or abstention means in your own pipeline before grading outputs, so the key measures the behavior your workflow actually needs.
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Build questions that recreate the accidents
Ordinary examples show whether the model can handle routine inputs, but they do little to reveal whether it will make the high-consequence mistake you identified. Build the exam around plausible traps first, then add a smaller set of normal cases.
Test confusable items and plausible non-targets
Include pairs that look similar but require different decisions, along with plausible items that should not be selected or acted on. These expose whether the model is matching surface similarity rather than the distinctions that matter in your reference data.
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Test changes and reversals within a message
Include inputs where an early statement is corrected or superseded later. The expected behavior should reflect the final relevant information, not an initial interpretation that the rest of the message changes.
Test memory only where the system uses it
For systems with memory, create cases where fresh information must override remembered information and cases where memory must not alter the verdict. Both matter: a system can fail by clinging to stale context or by letting irrelevant context contaminate a new decision.
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Cover each accident type with at least one question, adding variants when useful. Let the accident list determine the exam’s size rather than choosing an arbitrary target number. A handful of normal cases can provide context, but they should not crowd out the traps tied to irreversible harm.
Write and audit the answer key
For each question, write the expected behavior and check whether the reference data supports exactly one answer. The key is not simply the answer you hope the model will give: it must reflect what the available evidence actually determines.
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- If the reference data fixes one answer, record that answer and the evidence that supports it.
- If the input or reference data is ambiguous, key the expected behavior as needing confirmation or human handoff.
- If a model disagrees with the key, inspect the data and question before deciding that the model is at fault.
That last check matters because an evaluation can grade the model incorrectly when its own reference data is incomplete or contradictory. The reusable exam supplies a testing structure; the domain-specific key must be written for your pipeline. Green’s summary is: “The exam you may steal. The answer key you have to write yourself.”
Grade results, retain outputs, and document coverage
Count outcomes by severity grade rather than reducing the entire exam to one score. A single aggregate can obscure whether a system made a high-consequence mistake or merely requested an unnecessary confirmation.
Keep the raw answer sheets. If the grading criteria change, the team can review existing outputs against the revised rules without rerunning the model. Record what the exam did verify, which production data it did not test, and which uncertain cases must go to a human.
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A passing result applies only to the cases the team imagined and included. Green’s article says the described exam had not tested the author’s production data and that its example sentences were invented; it does not establish independent cross-domain validation or quantify any reduction in incidents. Treat the method as practical guidance for designing an evaluation, not proof that a deployment is safe. As Green warns, “Without line three, an exam score is just a number — the first unimagined sentence causes the accident.”
Where to find the original method and tool
Green published the method in “Steal This Exam. Here’s How to Port It to Your Own Pipeline” on DEV Community on August 25, 2026. The article links a public test runner and grader at github.com/ramses203/llm-test-harness. The described approach does not depend on using that repository; the core work is defining the failure, writing realistic cases, and auditing the key.
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