Evaluations and datasets · Lesson 16 of 18 · 8 min

Make a release recommendation

A release recommendation is a decision against recorded gates. Separate evidence that passed, evidence that failed and any untested conditions.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-evals-datasets-016.json. Separate the recorded facts from your assumptions.
Hint & worked example
A release recommendation is a decision against recorded gates. Separate evidence that passed, evidence that failed and any untested conditions.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-evals-datasets-016.json
```
Common mistake to avoid: Quality passing makes safety and load gates optional.
2
Isolate the deciding signal
Select the safety_gate field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Hold the release: safety failed and load behavior is untested.
A successful JSON query isolates safety_gate.
```
jq .safety_gate /opt/ai-lab/cases/aiops-evals-datasets-016.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-release, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-evals-datasets-016-decision.txt.
Hint & worked example
Hold the release: safety failed and load behavior is untested.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'hold-release' > /home/learner/lab/aiops-evals-datasets-016-decision.txt
```
Common mistake to avoid: Quality passing makes safety and load gates optional.

Understand the pattern

A release recommendation is a decision against recorded gates. Separate evidence that passed, evidence that failed and any untested conditions.

Interpret the evidence

Hold the release: safety failed and load behavior is untested.

A tempting mistake

Quality passing makes safety and load gates optional.

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-016.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "quality_gate": "pass",
  "safety_gate": "fail",
  "load_test": "not_run"
}

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

$ jq .safety_gate /opt/ai-lab/cases/aiops-evals-datasets-016.json

"fail"

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

$ printf '%s\n' 'hold-release' > /home/learner/lab/aiops-evals-datasets-016-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-release” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Make a release recommendation” justify?Make the change now and gather the missing evidence afterward.Hold the release: safety failed and load behavior is untested.Quality passing makes safety and load gates optional.

Apply it elsewhere

A colleague concludes: “Quality passing makes safety and load gates optional.” 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.

Next: Archive evaluation evidence Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision