Evaluations and datasets · Lesson 7 of 18 · 9 min

Inspect failed cases

Failed cases are diagnostic evidence. Group failures by behavior and inspect examples before changing a prompt, model or threshold.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-evals-datasets-007.json. Separate the recorded facts from your assumptions.
Hint & worked example
Failed cases are diagnostic evidence. Group failures by behavior and inspect examples before changing a prompt, model or threshold.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-evals-datasets-007.json
```
Common mistake to avoid: Raise the score threshold until the failing example disappears.
2
Isolate the deciding signal
Select the failure_type field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Investigate the unsupported claim and missing evidence for case-044.
A successful JSON query isolates failure_type.
```
jq .failure_type /opt/ai-lab/cases/aiops-evals-datasets-007.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: inspect-unsupported-claim, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-evals-datasets-007-decision.txt.
Hint & worked example
Investigate the unsupported claim and missing evidence for case-044.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'inspect-unsupported-claim' > /home/learner/lab/aiops-evals-datasets-007-decision.txt
```
Common mistake to avoid: Raise the score threshold until the failing example disappears.

Understand the pattern

Failed cases are diagnostic evidence. Group failures by behavior and inspect examples before changing a prompt, model or threshold.

Interpret the evidence

Investigate the unsupported claim and missing evidence for case-044.

A tempting mistake

Raise the score threshold until the failing example disappears.

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-007.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "failed_case": "case-044",
  "failure_type": "unsupported_claim",
  "output_has_citation": false
}

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

$ jq .failure_type /opt/ai-lab/cases/aiops-evals-datasets-007.json

"unsupported_claim"

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

$ printf '%s\n' 'inspect-unsupported-claim' > /home/learner/lab/aiops-evals-datasets-007-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 “inspect-unsupported-claim” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect failed cases” justify?Make the change now and gather the missing evidence afterward.Investigate the unsupported claim and missing evidence for case-044.Raise the score threshold until the failing example disappears.

Apply it elsewhere

A colleague concludes: “Raise the score threshold until the failing example disappears.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

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Keep learning

Case methodology: evidence, limits, decision