Evaluations and datasets · Lesson 18 of 18 · 5 min

Evaluations checkpoint

A complete evaluation review reconciles coverage, comparable versions, critical slices and release thresholds. A score is evidence, not a substitute for that decision.

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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-018.json. Separate the recorded facts from your assumptions.
Hint & worked example
A complete evaluation review reconciles coverage, comparable versions, critical slices and release thresholds. A score is evidence, not a substitute for that decision.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-evals-datasets-018.json
```
Common mistake to avoid: Promote every candidate whose average increases.
2
Isolate the deciding signal
Select the critical_regression field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Hold promotion while investigating the critical regression, despite the improved average.
A successful JSON query isolates critical_regression.
```
jq .critical_regression /opt/ai-lab/cases/aiops-evals-datasets-018.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: hold-critical-regression, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-evals-datasets-018-decision.txt.
Hint & worked example
Hold promotion while investigating the critical regression, despite the improved average.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'hold-critical-regression' > /home/learner/lab/aiops-evals-datasets-018-decision.txt
```
Common mistake to avoid: Promote every candidate whose average increases.

Understand the pattern

A complete evaluation review reconciles coverage, comparable versions, critical slices and release thresholds. A score is evidence, not a substitute for that decision.

Interpret the evidence

Hold promotion while investigating the critical regression, despite the improved average.

A tempting mistake

Promote every candidate whose average increases.

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-018.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "same_dataset": true,
  "same_scorer": true,
  "critical_regression": true,
  "average_improved": true
}

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

$ jq .critical_regression /opt/ai-lab/cases/aiops-evals-datasets-018.json

true

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

$ printf '%s\n' 'hold-critical-regression' > /home/learner/lab/aiops-evals-datasets-018-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 “hold-critical-regression” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Evaluations checkpoint” justify?Promote every candidate whose average increases.Make the change now and gather the missing evidence afterward.Hold promotion while investigating the critical regression, despite the improved average.

Apply it elsewhere

A colleague concludes: “Promote every candidate whose average increases.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

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01Archive evaluation evidenceArchived evaluation evidence must preserve the reproducible inputs and results. Sensitive raw examples may need controlled storage rather than inclusion in a public report.Practice

Case methodology: evidence, limits, decision