Evaluations and datasets · Lesson 2 of 18 · 9 min

Read evaluation cases

An evaluation case needs a task input, expected behavior and scoring rule. A reference answer alone is not enough when multiple valid outputs exist.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-evals-datasets-002.json. Separate the recorded facts from your assumptions.
Hint & worked example
An evaluation case needs a task input, expected behavior and scoring rule. A reference answer alone is not enough when multiple valid outputs exist.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-evals-datasets-002.json
```
Common mistake to avoid: Reject every answer whose wording differs from a single reference sentence.
2
Isolate the deciding signal
Select the rubric field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Score the required condition and unsupported claims, not just exact wording.
A successful JSON query isolates rubric.
```
jq .rubric /opt/ai-lab/cases/aiops-evals-datasets-002.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: score-against-rubric, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-evals-datasets-002-decision.txt.
Hint & worked example
Score the required condition and unsupported claims, not just exact wording.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'score-against-rubric' > /home/learner/lab/aiops-evals-datasets-002-decision.txt
```
Common mistake to avoid: Reject every answer whose wording differs from a single reference sentence.

Understand the pattern

An evaluation case needs a task input, expected behavior and scoring rule. A reference answer alone is not enough when multiple valid outputs exist.

Interpret the evidence

Score the required condition and unsupported claims, not just exact wording.

A tempting mistake

Reject every answer whose wording differs from a single reference sentence.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-evals-datasets-002.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "input": "summarize policy",
  "expected": "retain eligibility condition",
  "rubric": "condition present; no unsupported claims"
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .rubric /opt/ai-lab/cases/aiops-evals-datasets-002.json

"condition present; no unsupported claims"

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'score-against-rubric' > /home/learner/lab/aiops-evals-datasets-002-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “score-against-rubric” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Read evaluation cases” justify?Score the required condition and unsupported claims, not just exact wording.Reject every answer whose wording differs from a single reference sentence.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Reject every answer whose wording differs from a single reference sentence.” What should you do?Accept it because the training command completed.Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Validate a dataset schema Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision